Publications

2024

1. Sorbo,  S., Blakseth, S. S., Rasheed, A., Kvamsdal, T. and San, O. Enhancing elasticity models with deep learning: A novel corrective source term approach for accurate predictions, Applied Soft Computing, 153, 111312, 2024.

2. Mrigank, D., San, O. and Staples, A. Accelerated evolution of Burgers turbulence with coarse projective integration and deep learning. Physics of Fluids, 36, 035144, 2024.

3. Romeo, S., Oz, F., Kassem, A., Kara, K. and San, O. An augmented physics informed neural network approach for blunt-body dynamics, Physics of Fluids, 36, 017140, 2024.

2023

4. San, O., Pawar, S. and Rasheed, A. Decentralized digital twins of complex dynamical systems, Scientific Reports, 13 (1), 20087, 2023.

5. Stadtmann, F., Rasheed, A., Kvamsdal, T ., Johannessen, K. A., San, O. and more. Digital Twins in Wind Energy: Emerging Technologies and Industry-Informed Future Directions,  IEEE Access,  11, 110762-110795, 2023.

6. Ahmed, S. E. and San, O. Forward sensitivity analysis and mode dependent control for closure modeling of Galerkin systems.  Computers & Mathematics with Applications, 145, 289-302, 2023. [GitHub]

7. Ahmed, S. E., San, O., Lakshmivarahan, S. and Lewis, J. M. On the dual advantage of placing observations through forward sensitivity analysis.  Proceedings of the Royal Society A, 479, 20220815, 2023. [GitHub]

8. Mou, C., Merzari, E., San, O. and Iliescu, T. Energy-Based Lengthscale for Reduced Order Models of Turbulent Flows. Nuclear Engineering and Design, 412, 112454, 2023. 

9. Oz, F., San, O. and Kara, K. An efficient quantum partial differential equation solver with Chebyshev points.  Scientific Reports, 13, 7767, 2023.

10. Ahmed, S. E., San, O., Rasheed, A., Iliescu, T.  and Veneziani A. Physics guided machine learning for variational multiscale reduced order modeling.  SIAM Journal on Scientific Computing, 45, B283-B313, 2023. [GitHub]

11. Akbari, S., Dabaghian, P. and San, O. Blending machine learning and sequential data assimilation over latent spaces for surrogate modeling of Boussinesq systems. Physica D: Nonlinear Phenomena, 448, 133711, 2023. [GitHub]

12. Elfarri, E. M., Rasheed, A. and San, O. Artificial intelligence-driven digital twin of a modern house demonstrated in virtual reality.  IEEE Access, 11,  35035 - 35058, 2023. 

13. Akbari, S., Pawar, S. and San, O. Numerical assessments of a nonintrusive surrogate model based on recurrent neural networks and proper orthogonal decomposition: Rayleigh Bénard convection. International Journal of Computational Fluid Dynamics, 36, 599-617, 2023. [GitHub]

14. Dabaghian, P., Ahmed, S. and San, O. Nonintrusive reduced order modelling of convective Boussinesq flows. International Journal of Computational Fluid Dynamics, 36, 578-598, 2023. 

15. Mousavi, A., Bahrami, A., San, O. and Batra, R. Analysis of radial expansion, inversion and cavitation of rubberlike functionally graded material spheres. Mathematics and Mechanics of Solids, 28, 208-228, 2023.

16. Shahzad, Y., Bradshaw, C., Kamalapurkar, R. and San, O. Investigating critical model input features for unitary air conditioning equipment. Energy and Buildings, 284, 112823, 2023.

2022

17. Pawar, S., San, O., Rasheed, A. and Vedula, P. Frame invariant neural network closures for Kraichnan turbulence, Physica A: Statistical Mechanics and its Applications, 609, 128327, 2022.

18. San, O., Pawar, S. and Rasheed, A. Variational multiscale reinforcement learning for discovering reduced order closure models of nonlinear spatiotemporal transport systems, Scientific Reports, 12, 17947, 2022.

19. Pawar, S. and San, O.  Equation-free surrogate modeling of geophysical flows at the intersection of machine learning and data assimilation. Journal of Advances in Modeling Earth Systems, 14, e2022MS003170, 2022

20. Pawar, S., Sharma, A., Vijayakumar, G., Bay, C. J., Yellapantula, S. and San, O. Towards multi-fidelity deep learning of wind turbine wakes. Renewable Energy, 200:867–879, 2022.

21. Blakseth, S. S., Rasheed, A., Kvamsdal, T. and San, O. Combining physics-based and data-driven techniques for reliable hybrid analysis and modeling using the corrective source term approach, Applied Soft Computing, 128, 109533, 2022.

22. San, O., Pawar, S. and Rasheed, A. Prospects of federated machine learning in fluid dynamics, AIP Advances, 12, 095212, 2022.

23. Robinson, H., Pawar, S., Rasheed, A. and San, O. Physics guided neural networks for modelling of non-linear dynamics, Neural Networks, 154, 333-345, 2022.

24. Ahmed, S. E., Dabaghian, P., San, O., Bistrian, D. A. and Navon, I. M. Dynamic mode decomposition with core sketch. Physics of Fluids, 34, 066603, 2022. 

25. Pawar, S., San, O., Vedula, P., Rasheed, A. and Kvamsdal, T. Multi-fidelity information fusion with concatenated neural networks.  Scientific Reports, 12, 5900, 2022.

26. Heiberg,  A., Larsen, T. N., Meyer, E., Rasheed, A., San, O. and Varagnolo, D. Risk-based implementation of COLREGs for autonomous surface vehicles using deep reinforcement learning.  Neural Networks, 152, 17-33, 2022.

27. Blakseth, S. S., Rasheed, A., Kvamsdal, T. and San, O. Deep neural network enabled corrective source term approach to hybrid analysis and modeling.  Neural Networks, 146, 181-199, 2022.

2021

28. San, O. The digital twin revolution. Nature Computational Science, 1, 307-308, 2021. https://doi.org/10.1038/s43588-021-00077-0.

29. Ahmed, S. E., San, O., Rasheed, A. and Iliescu, T. Nonlinear proper orthogonal decomposition for convection-dominated flows.   Physics of Fluids, 33, 121702, 2021. [GitHub]

30. Ahmed, S. E., Pawar, S., San, O., Rasheed, A., Iliescu, T. and Noack, B. R. On closures for reduced order models – A spectrum of first-principle to machine-learned avenues.   Physics of Fluids, 33, 091301, 2021.

31. Pawar, S., San, O., Rasheed, A. and Navon, I. M. A nonintrusive hybrid neural-physics modeling of incomplete dynamical systems: Lorenz equations. GEM - International Journal on Geomathematics, 12, 17, 2021. [GitHub]

32. Pawar, S., San, O., Aditya, N., Rasheed, A. and Kvamsdal, T. Model fusion with physics-guided machine learning: projection based reduced order modeling. Physics of Fluids, 33, 067123, 2021. (Editor's Pick) [GitHub]

33. San, O., Rasheed, A. and Kvamsdal, T. Hybrid analysis and modeling, eclecticism, and multifidelity computing toward digital twin revolution. GAMM Mitteilungen - Surveys for Applied Mathematics and Mechanics, 44, e202100007, 2021. 

34. Sundby, T., Graham, J. M., Rasheed, A., Tabib, M. and San, O. Geometric change detection in digital twins. Digital, 1 (2), 111-129, 2021.

35. Ahmed, S. E., Pawar, S.,  San, O., Rasheed, A. and Tabib, M. A nudged hybrid analysis and modeling approach for realtime wake-vortex transport and decay prediction. Computers and Fluids, 221, 104895, 2021.

36. Stavelin, H., Rasheed, A., San, O. and Hestnes, A. J. Applying object detection to marine data and exploring explainability of a fully convolutional neural network using principal component analysis.  Ecological Informatics, 62, 101269, 2021.

37. Ahmed, S. E., San, O., Kara, K., Younis, R. and Rasheed, A. Multifidelity computing for coupling full and reduced order models. PLOS ONE, 16(2), e0246092, 2021. [GitHub]

38. Havenstrøm, S. T., Rasheed, A. and San, O. Deep reinforcement learning controller for 3D path following and collision avoidance by autonomous underwater vehicles. International Journal on Geomathematics in Robotics and AI, 7, 566037, 2021.

39. Pawar, S., San, O., Aksoylu, B., Rasheed, A. and Kvamsdal, T. Physics guided machine learning using simplified theories. Physics of Fluids, 33, 011701, 2021. [GitHub]

40. Pawar, S. and San, O. Data assimilation empowered neural network parameterizations for subgrid processes in geophysical flows. Physical Review Fluids, 6, 050501, 2021. [GitHub]

41. Mou, C., Koc, B., San, O., Rebholz, L. G. and Iliescu, T. Data-driven variational multiscale reduced order models. Computer Methods in Applied Mechanics and Engineering, 373, 113470, 2021. 

2020

42. Ahmed, S. E., Pawar, S. and San, O. PyDA: A hands-on introduction to dynamical data assimilation with Python. Fluids, 5(4), 225, 2020. [GitHub]

43. Ahmed, S. E., San, O., Kara, K., Younis, R. and Rasheed, A. Interface learning of multiphysics and multiscale systems. Physical Review E, 102, 053304, 2020. [GitHub]

44. Ahmed, S. E., Bhar, K., San, O. and Rasheed, A. Forward sensitivity approach for estimating eddy viscosity closures in nonlinear model reduction. Physical Review E, 102, 043302, 2020.  [GitHub]

45. Pawar, S., Ahmed, S. E. and San, O. Interface learning in fluid dynamics: statistical inference of closures within micro-macro coupling models. Physics of Fluids, 32, 091704, 2020. (Featured Article) [GitHub]

46. Meyer, E., Heiberg, A., Rasheed, A. and San, O. COLREG-Compliant Collision Avoidance for Unmanned Surface Vehicle using Deep Reinforcement Learning. IEEE Access,  8, 165344-165364, 2020. 

47. Ahmed, M., Park, H., Bach, C. K. and San, O. Numerical Investigation of Air Mixer for HVAC Testing Applications (ASHRAE RP-1733). Science and Technology for the Built Environment, 26(9), 1252-1273, 2020. 

48. Pawar, S., Ahmed, S. E., San, O., Rasheed, A. and Navon I. M. Long short-term memory embedded nudging schemes for nonlinear data assimilation of geophysical flows. Physics of Fluids, 32, 076606, 2020. [GitHub]

49. Pawar, S., Ahmed, S. E., San, O. and Rasheed, A. An evolve-then-correct reduced order model for hidden fluid dynamics. Mathematics, 8(4), 570, 2020. [GitHub]

50. Ahmed, S. E., San, O., Rasheed, A. and Iliescu, T. A long short-term memory embedding for hybrid uplifted reduced order models. Physica D: Nonlinear Phenomena, 409, 132471, 2020. [GitHub]

51. Maulik, R. and San, O. Numerical assessments of a parametric implicit large eddy simulation model. Journal of Computational and Applied Mathematics, 376, 112866, 2020. 

52. Maulik, R., San, O. and Jacob, J. D. Spatiotemporally dynamic implicit large eddy simulation using machine learning classifiers. Physica D: Nonlinear Phenomena, 406, 132409, 2020. 

53. Pawar, S., Ahmed, S. E., San, O. and Rasheed, A. Data-driven recovery of hidden physics in reduced order modeling of fluid flows. Physics of Fluids, 32, 036602, 2020. 

54. Meyer, E., Robinson, H., Rasheed, A. and San, O. Taming an autonomous surface vehicle for path following and collision avoidance using deep reinforcement learning. IEEE Access,  8, 41466-41481, 2020. 

55. Ahmed, S. E. and San, O. Breaking the Kolmogorov barrier in model reduction of fluid flows. Fluids, 5, 26, 2020. 

56. Ahmed, S. E., San, O., Bistrian, D. A. and Navon, I. M. Sampling and resolution characteristics in reduced order models of shallow water equations: intrusive vs non‐intrusive. International Journal for Numerical Methods in Fluids, 92 (8), 992-1036, 2020. 

57. Rasheed, A., San, O. and Kvamsdal, T. Digital twin: values, challenges and enablers from a modeling perspective. IEEE Access, 8, 21980-22012, 2020. 

58. Pawar, S., San, O., Rasheed, A. and Vedula, P. A priori analysis on deep learning of subgrid-scale parameterizations for Kraichnan turbulence. Theoretical and Computational Fluid Dynamics, 34, 429-455, 2020. 

59. Vaddireddy, H., Rasheed, A., Staples, A. E. and San, O. Feature engineering and symbolic regression methods for detecting hidden physics from sparse sensor observation data. Physics of Fluids, 32, 015113, 2020.  (Editor's Pick) [GitHub]

2019

60. Ahmed, S. E., Rahman, S. M., San, O., Rasheed, A. and Navon, I. M. Memory embedded non-intrusive reduced order modeling of non-ergodic flows. Physics of Fluids, 31, 126602, 2019.  

61. Rahman, S. M., Pawar, S., San, O., Rasheed, A. and Iliescu, T. Nonintrusive reduced order modeling framework for quasigeostrophic turbulence. Physical Review E, 100, 053306, 2019. 

62. Pawar, S., Rahman, S. M., Vaddireddy, H., San, O., Rasheed, A. and Vedula, P. A deep learning enabler for non-intrusive reduced order modeling of fluid flows. Physics of Fluids, 31, 085101, 2019. (Featured Article) [GitHub]

Highlighted by Savannah Mandel, Scilight, American Institute of Physics: Non-intrusive approach to reducedorder modeling of fluid flows.

63. Pawar, S. and San, O. CFD Julia: A learning module structuring an introductory course on computational fluid dynamics. Fluids, 4(3), 159, 2019. [GitHub]

64. Vaddireddy, H. and San, O. Equation discovery using fast function extraction: a deterministic symbolic regression approach. Fluids, 4(2), 111, 2019.

65. Rahman, S. M., Ahmed, S. and San, O. A dynamic closure modeling framework for model order reduction of geophysical flows. Physics of Fluids, 31, 046602, 2019. 

66. Maulik, R., San, O., Jacob, J. and Crick, C. Sub-grid scale model classification and blending through deep learning. Journal of Fluid Mechanics, 870, 784-812, 2019. 

67. San, O., Maulik, R. and Ahmed, M. An artificial neural network framework for reduced order modeling of transient flows. Communications in Nonlinear Science and Numerical Simulation, 77, 271-287, 2019. 

68. Rahman, S. M. and San, O. A relaxation filtering approach for two-dimensional Rayleigh–Taylor instability-induced flows. Fluids, 4(2), 78, 2019.

69. Maulik, R., San, O., Rasheed, A. and Vedula, P. Sub-grid modelling for two-dimensional turbulence using neural networks. Journal of Fluid Mechanics, 858, 122-144, 2019. 

70. Rahman, S. M. and San, O. A localized dynamic closure model for Euler turbulence. International Journal of Computational Fluid Dynamics, 32(8-9), 326-378, 2019. 

2018

71. Maulik, R., San, O., Rasheed, A. and Vedula, P. Data-driven deconvolution for large eddy simulations of Kraichnan turbulence. Physics of Fluids, 30, 125109, 2018. 

72. Rahman, S. M., San, O. and Rasheed, A. A hybrid approach for model order reduction of barotropic quasi-geostrophic turbulence. Fluids, 3(4), 86, 2018. 

73. Ahmed, M. and San, O. Stabilized principal interval decomposition method for model reduction of nonlinear convective systems with moving shocks. Computational and Applied Mathematics, 37(5), 6870-6902, 2018. 

74. Kholikov, K., Ilhom, S., Sajjad, M., Smith, M. E., Monroe, J. D., San, O. and Er, A. O. Improved singlet oxygen generation and antimicrobial activity of sulphur-doped graphene quantum dots coupled with methylene blue for photodynamic therapy applications. Photodiagnosis and Photodynamic Therapy, 24, 7-14, 2018. 

75. Rahman, S. M., Rasheed, A. and San, O. A hybrid analytics paradigm combining physics-based modeling and data-driven modeling to accelerate incompressible flow solvers. Fluids, 3(3), 50, 2018. 

76. San, O. and Maulik, R. Stratified Kelvin–Helmholtz turbulence of compressible shear flows. Nonlinear Processes in Geophysics, 25, 457-476, 2018. 

77. San, O. and Maulik, R. Extreme learning machine for reduced order modeling of turbulent geophysical flows. Physical Review E, 97, 042322, 2018.

78. San, O. and Maulik, R. Neural network closures for nonlinear model order reduction. Advances in Computational Mathematics, 44(6), 1717-1750, 2018. 

79. Maulik, R., San, O. and Behera, R., An adaptive multilevel wavelet framework for scale-selective WENO reconstruction schemes. International Journal for Numerical Methods in Fluids, 87(5), 239-269, 2018. 

80. Ilhom, S., Seyitliyev, D., Kholikov, K., Thomas, Z., Er, A. O., Li, P., Karaca, H. E. and San, O. Laser shock wave-assisted patterning on NiTi shape memory alloy surfaces. Shape Memory and Superelasticity, 4(1), 224-231, 2018. 

81. Ilhom, S., Kholikov, K., Li, P., Ottman, C., Sanford, D., Thomas, Z., San, O., Karaca, H. E. and Er, A. O. Scalable patterning using laser-induced shock waves. Optical Engineering, 57(4), 041413, 2018. 

82. San, O. and Maulik, R. Machine learning closures for model order reduction of thermal fluids. Applied Mathematical Modelling, 60, 681-710, 2018. 

83. Maulik, R. and San, O. A dynamic closure modeling framework for large eddy simulation using approximate deconvolution: Burgers equation. Cogent Physics, 5, 1464368, 2018.

84. San, O. and Vedula, P. Generalized deconvolution procedure for structural modeling of turbulence. Journal of Scientific Computing, 75(2), 1187-1206, 2018.

85. Maulik, R. and San, O. Explicit and implicit LES closures for Burgers turbulence. Journal of Computational and Applied Mathematics, 327, 12-40, 2018.

2017

86. Maulik, R. and San, O. A neural network approach for the blind deconvolution of turbulent flows. Journal of Fluid Mechanics, 831, 151-181, 2017.

87. Seyitliyev, D., Kholikov, K., Grant, B., San, O. and Er, A. O. Laser-induced hydrogen generation from graphite and coal. International Journal of Hydrogen Energy, 42, 26277-26288, 2017.

88. Maulik, R. and San, O. A novel dynamic framework for subgrid scale parametrization of mesoscale eddies in quasigeostrophic turbulent flows. Computers & Mathematics with Applications, 74, 420-445, 2017. 

89. Benosman, M., Borggaard, J., San, O. and Kramer, B. Learning-based robust stabilization for reduced-order models of 2D and 3D Boussinesq equations. Applied Mathematical Modelling, 49, 162-181, 2017.

90. Maulik, R. and San, O. Resolution and energy dissipation characteristics of implicit LES and explicit filtering models for compressible turbulence. Fluids, 2(14), 14, 2017.

91. Maulik, R. and San, O. A dynamic framework for functional parameterisations of the eddy viscosity coefficient in two-dimensional turbulence. International Journal of Computational Fluid Dynamics, 31(2), 69-92, 2017.

92. Maulik, R. and San, O. A stable and scale-aware dynamic modeling framework for subgrid-scale parameterizations of two-dimensional turbulence. Computers & Fluids, 158, 11-38, 2017. 

93. Maulik, R. and San, O. A dynamic subgrid-scale modeling framework for Boussinesq turbulence. International Journal of Heat and Mass Transfer, 108, 1656-1675, 2017.

2016

94. Maulik, R. and San, O. Dynamic modeling of the horizontal eddy viscosity coefficient for quasigeostrophic ocean circulation problems. Journal of Ocean Engineering and Science, 1(4), 300-324, 2016.

95. San, O. Numerical assessments of ocean energy extraction from western boundary currents using a quasi-geostrophic ocean circulation model. International Journal of Marine Energy, 16, 12-29, 2016. 

96. San, O. Analysis of low-pass filters for approximate deconvolution closure modelling in one-dimensional decaying Burgers turbulence. International Journal of Computational Fluid Dynamics, 30, 20-37, 2016. 

2009-2015

97. San, O. and Borggaard, J. Principal interval decomposition framework for POD reduced-order modeling of convective Boussinesq flows. International Journal For Numerical Methods in Fluids, 78, 37-62, 2015.

98. San, O. and Iliescu, T. A stabilized proper orthogonal decomposition reduced-order model for large scale quasigeostrophic ocean circulation. Advances in Computational Mathematics, 41, 1289-1319, 2015.

99. San, O. A novel high-order accurate compact stencil Poisson solver: application to cavity flows. International Journal of Applied Mechanics, 7, 1550006, 2015.

100. San, O., Staples, A. E. and Iliescu, T. A posteriori analysis of low-pass spatial filters for approximate deconvolution large eddy simulations of homogeneous incompressible flows International Journal of Computational Fluid Dynamics, 29, 40-66, 2015.

101. San, O. and Kara, K. Evaluation of Riemann flux solvers for WENO reconstruction schemes: Kelvin-Helmholtz instability. Computers & Fluids, 117, 24-41, 2015.

102. San, O. A dynamic eddy-viscosity closure model for large eddy simulations of two-dimensional decaying turbulence. International Journal of Computational Fluid Dynamics, 28, 363-382, 2014.

103. San, O. and Kara, K. Numerical assessments of high-order accurate shock capturing schemes: Kelvin-Helmholtz type vortical structures in high-resolutions. Computers & Fluids, 89, 254-276, 2014.

104. San, O. and Iliescu, T. Proper orthogonal decomposition closure models for fluid flows: Burgers equation. International Journal of Numerical Analysis & Modeling, Series B, 5, 217-237, 2014. 

105. San, O. and Staples, A. E. A coarse-grid projection method for accelerating incompressible flow computations. Journal of Computational Physics, 233, 480-508, 2013. 

106. San, O. and Staples, A. E. An efficient coarse grid projection method for quasigeostrophic models of large-scale ocean circulation. International Journal For Multiscale Computational Engineering, 11, 463-495, 2013. 

107. San, O., Staples, A. E. and Iliescu, T. Approximate deconvolution large eddy simulation of a stratified two-layer quasigeostrophic ocean model. Ocean Modelling, 63, 1-20, 2013. 

108. San, O. and Staples, A. E. Stationary two-dimensional turbulence statistics using a Markovian forcing scheme. Computers & Fluids, 71, 1-18, 2013. 

109. San, O. and Staples, A. E. An improved model for reduced-order physiological fluid flows. Journal of Mechanics in Medicine and Biology, 12, 1250052, 2012. 

110. San, O. and Staples, A. E. Dynamics of pulsatile flows through elastic microtubes. International Journal of Applied Mechanics, 4, 1250006, 2012. 

111. San, O. and Staples, A. E. High-order methods for decaying two-dimensional homogeneous isotropic turbulence. Computers & Fluids, 63, 105-127, 2012. 

112. San, O., Staples, A. E., Wang, Z. and Iliescu, T. Approximate deconvolution large eddy simulation of a barotropic ocean circulation model. Ocean Modelling, 40, 120-132, 2011. 

113. San, O., Bayraktar, I. and Bayraktar, T. Size and expansion ratio analysis of micro nozzle gas flow. International Communications in Heat and Mass Transfer, 36, 402-411, 2009.

1. Snyder, W., Mou, C., San, O., De Vita, R., and Iliescu, T. Reduced Order Model Closures: A Brief Tutorial,  Recent Advances in Mechanics and Fluid-Structure Interaction with Applications, F. Carapau and A. Vaidya, editors, Springer Nature, Switzerland, ISBN: 978-3-031-14323-6, 2022. 

2. Ahmed, S. E., San, O. and Lakshmivarahan, S. Forward sensitivity analysis of the FitzHugh-Nagumo system: Parameter estimation. Advances in Nonlinear Dynamics, 93-103, 2022. Part of the NODYCON Conference Proceedings Series (NCPS).

3. Kurtulus, O., Bach, C. K., San, O., Maulik, R., Ziviani, D., Bradshaw, C. R. and Groll, E. A. Psychrometric performance testing for HVAC&R components and equipment. The Art of Measuring in Thermal Sciences, J. Meyer and M. de Paepe, editors, CRC Press–Taylor and Francis,Series: Heat Transfer, pages 215–238, 2020.

1. San, O. Recent numerical advances in fluid mechanics. Multidisciplinary Digital Publishing Institute, Bazel, Switzerland, ISBN:978-3-03936-403-9, 2020.

1. San O. Digital Twins: Challenges and Enablers. von Karman Institute / ULB Lecture Series Machine Learning for Fluid Mechanics: Analysis, Modeling, Control and Closures, 29 January- 02 February 2024.

2. San O. Hybrid physics-data analysis and modeling: an inductive bias perspective. SIAM SEAS, Virginia Tech, March 25, 2023.

3. San O. Hybrid physics-data analysis and modeling: an inductive bias perspective. Machine Learning + X seminars, The Crunch Group, Brown University, January 13, 2023.

4. San, O. Hybrid physics-data analysis and modeling: a key technology for developing digital twins. Mechanical Engineering Fall Seminar Series, Rice University, November 30, 2022.

5. San, O. Hybrid modeling in fluid dynamics. Harvard Widely Applied Mathematics (WAM) Seminar Talk, The Harvard John A. Paulson School of Engineering and Applied Sciences, November 10, 2022.

6. San, O.  Hybrid physics-data modeling in fluid dynamics. e-Seminar on Scientific Machine Learning,  SciML Google Group, October 23, 2022.

7. San, O. The prospects of hybrid analysis and modeling in fluid dynamics. The 1st International Forum for Artificial Intelligence in Mechanical Engineering (IFAIME 2022), July 25–30, 2022.

8. San, O. Hybrid analysis and modeling. Center of Excellence Collaboration Workshop, Trondheim, Norway, May 26, 2021.

9. San, O. Data-driven closure modeling of fluid flows, Department of Scientific Computing, Florida State University, January 31, 2020.

10. San, O. Data-driven closure modeling in transport phenomena. Graduate Seminar Series, The McDougall School of Petroleum Engineering, University of Tulsa, Tulsa, OK, February 14, 2020.

1. Tabib, M. V., Tsiolakis, V., Pawar, S., Ahmed, S. E., Rasheed, A., Kvamsdal, T., and San, O. Hybrid deep-learning POD-based parametric reduced order model for flow around wind-turbine blade.  In Journal of Physics: Conference Series 2362 (1), 012039, 2022.

2. Rasheed, A., Kvamsdal, T., and San, O. Hybrid Analysis and Modeling as an Enabler for Digital Twins.  The ECCOMAS Congress 2022 Newsletter. The 8th European Congress on Computational Methods in Applied Sciences and Engineering, Oslo, Norway, on 5th – 9th of June, 2022. 

3. Ahmed, S. E., San, O., Bistrian, D. A. and Navon, I. M.  Sketching Methods for Dynamic Mode Decomposition in Spherical Shallow Water Equations. AIAA SciTech 2022. 3–7 January 2022. [Video]

4. Pawar, S., San, O.  and Yen, G. G. Hyperparameter Search using the Genetic Algorithm for Surrogate Modeling of Geophysical Flows. AIAA SciTech 2022.  3–7 January 2022. [Video]

5. Mou, C., Merzari, E., San, O. and Iliescu, T. A numerical investigation of the lengthscale in the mixing-length reduced order model of the turbulent channel flow. 19th International Topical Meeting on Nuclear Reactor Thermal Hydraulics (NURETH-19), Brussels, Belgium, March 6 - 11, 2021.

6. Tabib, M. V., Pawar, S., Ahmed, S. E., Rasheed, A., and San, O. A non-intrusive parametric reduced order model for urban wind flow using deep learning and Grassmann manifold.  In Journal of Physics: Conference Series, volume 2018, page 012038. IOP Publishing, 2021.

7. Ahmed, S. E., San, O. and Lakshmivarahan, S. Forward sensitivity analysis of the FitzHugh-Nagumo system: Parameter estimation. The Second International Nonlinear Dynamics Conference (NODYCON 2021). February 16-19, 2021.

8. Pawar, S., Ahmed, S. E., San, O. and Rasheed, A. Hybrid analysis and modeling for next generation of digital twins. 19th Deep Sea Offshore Wind R&D Conference, EERA DeepWind’ 2021, 13-15 January 2021, Trondheim, Norway. (Best content poster award).

9. Ahmed, S. E., San, O. and Faruque, I. Nonlinear filtering for simultaneous state correction and eddy viscosity estimation in computational fluid dynamics. AIAA SciTech 2021. 11–15 & 19–21 January 2021.

10. Pawar, S. and San, O.  Comparative study of sequential data assimilation methods for the Kuramoto-Sivashinsky equation. AIAA SciTech 2021. 11–15 & 19–21 January 2021.

11. Park, H., Bach, C. and San, O. An update on the evaluation of air mixer performance. ASHRAE Transactions; Vol. 126 (2), pages 20–22, 2020.

12. Tran, D. T., Robinson, H., Rasheed, A., San, O., Tabib, M. and Kvamsdal, T. GANs enabled super-resolution reconstruction of wind field. Journal of Physics: Conference Series, volume 1669, page 012029. IOP Publishing, 2020.

13. Ahmed, S. E., Pawar, S., San, O. and Rasheed, A. Reduced order modeling of fluid flows: Machine learning, Kolmogorov barrier, closure modeling, and partitioning. AIAA AVIATION 2020. June 15–19, 2020.

14. Belekov, E., Cooper, L., Kholikov, K., San, O., and Er, A. O. Light induced bacterial deactivation using graphene quantum dot. Optical Methods for Tumor Treatment and Detection: Mechanisms and Techniques in Photodynamic Therapy XXIX, volume 11220, page 112200E, 2020.

15. Hossain, M. Y., Maulik, R, Park, H., Ahmed, M., Bach, C. K. and San, O. Improvement of unitary equipment and heat exchanger testing methods. ASHRAE Transactions; Vol. 125, pages 214–221, 2019.

16. Hossain, M. Y., Bach, C. K., San, O. and Yatim, A. Effect of inlet duct design on fan performance of indoor air handling unit. ASHRAE Transactions; Vol. 125, pages 40–42, 2019.

17. Hu, W. and San, O.  Optimal control of heat transfer in unsteady Stokes flows.  2018 IEEE Conference on Decision and Control (CDC), pages 3752–3757, 2018.

18. Ilhom, S., Kholikov, K., Li, P., Seyitliyev, D., Thomas, Z., Roberts, D., San, O., Karaca, H. E. and Er.  A. O. Formation of two-way shape memory effect in NiTi alloy using pulsed laser irradiation. Laser-based Micro-and Nanoprocessing XII, volume 10520, page 105200V, 2018.

19. Maulik, R., San, O. and Bach, C. K. A computational investigation of the effect of ground clearance in vertical ducting systems-ASHRAE RP-1743. Paper 3586, IHPBC 2018.

20. Hossain, M. Y., San, O. and Bach. C. K. Effect of inlet duct and damper design on fan performance and static pressure measurements (ASHRAE RP 1743). Paper 2659, IRACC 2018.

21. San, O. and Borggaard, J. Basis selection and closure for POD models of convection dominated Boussinesq flows.  21st International Symposium on Mathematical Theory of Networks and Systems, Groningen, Netherlands, July 7–11, 2014.

22. San, O., Yalcin, S. E. and Baysal, O. Comparing piezoelectric and electroosmotic micropumps for biomedical applications. IMECE2008-67343, pp. 877-884, 2008.

23. San, O. and Bayraktar, I. Numerical modeling of gas flow in converging-diverging micronozzles. 37th AIAA Fluid Dynamics Conference and Exhibit, Miami, FL, June 25–28, 2007.

1. Dhingra, M., Staples, A. and San, O. A deep learning based closure model for the multiscale evolution of Burgers turbulence. 75th Annual Meeting of the APS Division of Fluid Dynamics, Indianapolis, IN, November 20–22, 2022.

2. San, O., Pawar, S. Vedula, P., Rasheed, A. and Kvamsdal, T. Inductive bias and information fusion with concatenated neural networks. 75th Annual Meeting of the APS Division of Fluid Dynamics, Indianapolis, IN, November 20–22, 2022.

3. Dabaghian, P., H. and San, O.  Nonintrusive reduced order modeling of convective Boussinesq flows. 7th Annual Meeting of SIAM Central States Section, October 1-2, 2022, Oklahoma State University, Stillwater, OK (SIAM-CSS 2022). 

4. Akbari, S. and San, O.  Nonlinear proper orthogonal decomposition approach for modeling Rayleigh Bénard convection. 7th Annual Meeting of SIAM Central States Section, October 1-2, 2022, Oklahoma State University, Stillwater, OK (SIAM-CSS 2022). 

5. Zomorodiyan, M. and San, O.  Automatic Mixed-Precision (AMP) Computational Fluid Dynamics (CFD). 7th Annual Meeting of SIAM Central States Section, October 1-2, 2022, Oklahoma State University, Stillwater, OK (SIAM-CSS 2022). 

6. Romeo, S. and San, O.  Fluid flow modeling in elastic networking tubes. 7th Annual Meeting of SIAM Central States Section, October 1-2, 2022, Oklahoma State University, Stillwater, OK (SIAM-CSS 2022). 

7. Ahmed, S. E., San, O., Rasheed, A., Iliescu, T. and Veneziani, A. Hierarchical learning to reduced order modeling. AIAA Aviation Forum, Chicago, Illinois, 27 June - 1 July 2022.

8. Shah, I., Dasi, L., Iliescu, T., San, O., and Veneziani, A. Real-Time Shape Optimization of the Total Cavopulmonary Connection in Fontan Surgical Planning Via Reduced Order Modeling, 2022 Summer Biomechanics, Bioengineering, and Biotransport Conference, June 20th-23rd, 2022, Eastern Shore, Maryland.

9. Pawar, S. and San, O.  Physics-constrained deep learning for subgrid modeling in geophysical flows. 4th MAE Graduate Research Symposium, Oklahoma State University, Stillwater, OK, March 30, 2022.

10. Ahmed, S. E. and San, O.  Hierarchical learning to generate surrogate models in fluid dynamics. 4th MAE Graduate Research Symposium, Oklahoma State University, Stillwater, OK, March 30, 2022.

11. Zomorodiyan, M. and San, O.  Nonlinear proper orthogonal decomposition for compressible flows. 4th MAE Graduate Research Symposium, Oklahoma State University, Stillwater, OK, March 30, 2022.

12. Romeo, S. and San, O.  Modeling networking flows in elastic tubes. 4th MAE Graduate Research Symposium, Oklahoma State University, Stillwater, OK, March 30, 2022.

13. Ahmed, S. E., Pawar, S., San, O. and Rasheed, A. Towards physics-aware model reduction and data compression. The NSF AI Planning Institute for Data Driven Physics Workshop on AI Super-Resolution Simulations: from Climate Science to Cosmology,  Carnegie Mellon University, Pittsburgh, PA, February 23-25, 2022.

14. Pawar, S., San, O., and Rasheed, A. Physics-guided machine learning for surrogate modeling in fluid mechanics. 74th Annual Meeting of the APS Division of Fluid Dynamics, Phoenix, AZ, November 21–23, 2021.

15. Ahmed, S., San, O., Rasheed, A., Veneziani, A., and Iliescu, T. Physics-guided machine learning variational multiscale reduced order models. 74th Annual Meeting of the APS Division of Fluid Dynamics, Phoenix, AZ, November 21–23, 2021. (APS DFD travel grant).

16. Ahmed, S. E., San, O., Iliescu, T. and Veneziani, A. Combining machine learning and data assimilation for hybrid multi-level variational multiscale reduced order models. The Mechanistic Machine Learning and Digital Twins for Computational Science, Engineering & Technology Conference (MMLDT-CSET 2021), San Diego, CA September 26-29, 2021. (Invited).

17. Ahmed, S., San, O., Veneziani, A., and Iliescu, T. A hybrid variational multiscale and machine learning approach for nonlinear model order reduction. 44th SIAM Southeastern Atlantic Section Conference (SIAM-SEAS), Auburn, AL, September 18–19, 2021. (SIAM-SEAS travel grant).

18. Ahmed, S., Pawar, S., San, O., and Rasheed, A. Interface Learning: Towards Seamless Integration of Multi-scale, Multi-physics and Multi-fidelity (M3) Models. 16th U.S. National Congress on Computational Mechanics (USNCCM16), Virtual Event, July 25–29, 2021.

19. Pawar, S., San, O., and Rasheed, A. Physics guided machine learning in fluid dynamics. 16thU.S. National Congress on Computational Mechanics (USNCCM16), Virtual Event, July 25–29, 2021

20. Ahmed, S. E., San, O.,  Kara, K., Younis, R. and Rasheed, A. Interface learning for coupling full and reduced order models in multifidelity simulations. [Virtual] The 40th ASME/AIAA Online Regional Symposium (2021). April 3rd, 2021. (Best paper award).

21. Pawar, S. and San, O. Deep learning approaches for subgrid scale parameterization in chaotic dynamical systems. [Virtual] The 40th ASME/AIAA Online Regional Symposium (2021). April 3rd, 2021.

22. Ahmed, S. E., San, O. and Rasheed, A. Data assimilation for dynamic estimate of closure effect in reduced order models. [Virtual] SIAM CSE21 (2021). March 1-5, 2021.

23. Pawar, S., Ahmed, S. and San, O. Interface closure in multi-fidelity computing via deep learning. [Virtual] SIAM CSE21 (2021). March 1-5, 2021.

24. Pawar, S. and San, O. Data assimilation assisted neural network parameterizations for subgrid processes in multiscale systems. 73rd Annual Meeting of the APS Division of Fluid Dynamics, Virtual Event, 2020

25. Ahmed, S., Pawar, S., and San, O. Interface learning paradigms for multi-scale and multi-physics systems. 73rd Annual Meeting of the APS Division of Fluid Dynamics, Virtual Event, 2020

26. Pawar, S. and San, O.  Direct numerical simulation of turbulent flows in Python.  Poster presented at the Coalition for Advancing Digital Research & Education (CADRE) conference, Oklahoma State University, Stillwater, OK, April 17, 2020

27. Ahmed, S. E. and San, O.  Augmentation of nonlinear reduced order models via machine learning.  3rd MAE Graduate Research Symposium, Oklahoma State University, Stillwater, OK, February 28, 2020

28. Pawar, S., Ahmed, S. E., and San, O. Hybrid analysis and modeling paradigm for reduced order modeling of fluid flows with unknown physics. Poster presented at the 3rd MAE Graduate Research Symposium, Oklahoma State University, Stillwater, OK, February 28, 2020

29. Vaddireddy, H. and San, O. Blind deconvolution of turbulent flows using super-resolution reconstruction. Poster presented at the 3rd MAE Graduate Research Symposium, Oklahoma State University, Stillwater, OK, February 28, 2020

30. Bhar, K. and San, O. Reduced order modeling of shallow water equations using dynamic mode decomposition. Poster presented at the 3rd MAE Graduate Research Symposium, Oklahoma State University, Stillwater, OK, February 28, 2020

31. Annaorazov, M., Ahmed, S., San, O., and Turgut, E. A nonintrusive reduced order modelling of magnetic skyrmions. APS March Meeting 2020, Denver, Colorado, March 2–6, 2020

32. Pawar, S., Ahmed, S., Vaddireddy, H., San, O., Maulik, R., Tran, D. T., and Rasheed, A. Data-driven modeling for fluid dynamics: Turbulence closure, model order reduction, and super-resolution. Poster presented at the NSF Workshop on Exuberance of Machine Learning in Transport Phenomena, Southern Methodist University, Dallas, TX, February 10–11, 2020

33. Pawar, S., San, O., and Rasheed, A. Deep learning based sub-grid scale closure for LES of Kraichnan turbulence. 72nd Annual Meeting of the APS Division of Fluid Dynamics, Seattle, WA, November 23–26, 2019

34. Ahmed, S. E., Rahman†, S. M., San, O., and Rasheed, A. LSTM based nonintrusive ROM of convective flows. 72nd Annual Meeting of the APS Division of Fluid Dynamics, Seattle, WA, November 23–26, 2019

35. Vaddireddy, H. and San, O. A symbolic regression approach for the development of high accuracy defect correction schemes. 72nd Annual Meeting of the APS Division of Fluid Dynamics, Seattle, WA, November 23–26, 2019

36. Sterud, C., Robinson, H., Moe, S., Rasheed, A., and San, O. Deep reinforcement learning for path following and collision avoidance. 5th Norwegian Big Data Symposium (NOBIDS) 2019, Trondheim, Norway, November 13, 2019

37. Vaddireddy, H. and San, O. Model discovery using deterministic symbolic regression. 39th Oklahoma AIAA/ASME Symposium, University of Tulsa, Tulsa, OK, April 6, 2019

38. Ahmed, S. E. and San, O. Staggered reduced order model for shallow water equations: POD vs DMD. 39th Oklahoma AIAA/ASME Symposium, University of Tulsa, Tulsa, OK, April 6,2019

39. Pawar, S. and San, O.  Numerical assessment of higher order compact scheme for Poisson equation. 39th Oklahoma AIAA/ASME Symposium, University of Tulsa, Tulsa, OK, April 6, 2019

40. Ahmed, M., San, O., and Bach, C. K.  Improvement of air mixer performance for HVAC testing applications: CFD simulations (RP-1733). 39th Oklahoma AIAA/ASME Symposium, University of Tulsa, Tulsa, OK, April 6, 2019

41. Rahman, S. M. and San, O. On the development of robust reduced order model frameworks for partial differential equation systems: current status and future prospects. 39th Oklahoma AIAA/ASME Symposium, University of Tulsa, Tulsa, OK, April 6, 2019

42. Er, A., Kholikov, K., Cooper, L., Belekov, E., and San, O. Antimicrobial activity of sulphur-doped graphene quantum dots coupled with methylene blue for photodynamic therapy applications. APS March Meeting 2019, Boston, MA, March 4–8, 2019

43. Er, A. O., Ottman, C., Sanford, D., Belekov, E., Ilhom, S., Karaca, H., and San, O. Laser-induced shock wave-assisted patterning on NiTi shape memory alloys. International Society for Optics and Photonics, SPIE LASE, San Francisco, CA, 4 March, 2019

44. Maulik, R., San, O., Rasheed, A., and Vedula, P. Data-driven filter estimation for the sub-grid modelling of Kraichnan turbulence.  SIAM Conference on Computational Science and Engineering (CSE19), Spokane, WA, February 25–March 1, 2019

45. Rahman, S. M., San, O., and Rasheed, A.  A hybrid approach for model order reduction of barotropic quasi-geostrophic turbulence. SIAM Conference on Computational Science and Engineering (CSE19), Spokane, WA, February 25–March 1, 2019

46. San, O. Data-driven subgrid scale modeling of turbulence. Poster presented at the DOE ASCR Applied Mathematics Principal Investigators’ (PI) Meeting, Rockville, MD January 29–30, 2019

47. Maulik, R., San, O., Rasheed, A., and Vedula, P. Data-driven deconvolution for the large eddy simulation of Kraichnan turbulence. 71st Annual Meeting of the APS Division of Fluid Dynamics, Atlanta, GA, November 18–20, 2018

48. San, O., Rahman, S. M., and Rasheed, A. A hybrid analytics paradigm combining physics-based modeling and data-driven modeling to accelerate incompressible flow solvers. 71st Annual Meeting of the APS Division of Fluid Dynamics, Atlanta, GA, November 18–20, 2018

49. Belekov, E., Cooper, L., Devarakonda, K., Kholikov, K., Ilhom, S., Smith, M., Monroe, J., San, O., and Er, A.  Bacterial deactivation by using graphene quantum dot as an effective photodynamic therapy agent. 85th Annual Meeting of the APS Southeastern Section, Knoxville, TN, November 8–10, 2018

50. Maulik, R., San, O., Rasheed, A., and Vedula, P.  Physics-based artificial neural network formulations for LES of Kraichnan turbulence.  2nd MAE Graduate Research Symposium, Oklahoma State University, Stillwater, OK, November 2, 2018

51. Ahmed, M., San, O., and Bach, C. Air mixer design guidelines using CFD analysis (ASHRAE,RP-1733). Poster presented at the 2nd MAE Graduate Research Symposium, Oklahoma State University, Stillwater, OK, November 2, 2018

52. Hossain, M. Y., Bach, C., and San, O. Effect of inlet duct configuration on the fan performance of air handling units (ASHRAE RP-1743). Poster presented at the 2nd MAE Graduate Research Symposium, Oklahoma State University, Stillwater, OK, November 2, 2018

53. Park, H., Bach, C., and San, O. A review of designs and test strategies for air mixers (ASHRAERP-1733). Poster presented at the 2nd MAE Graduate Research Symposium, Oklahoma State University, Stillwater, OK, November 2, 2018

54. Rahman, S. M. and San, O. Hybrid analytics paradigm in fluid dynamics. Poster presented at the 2nd MAE Graduate Research Symposium, Oklahoma State University, Stillwater, OK, November 2, 2018

55. Vaddireddy, H. and San, O.  A survey of symbolic regression approaches to distill equations/models from big data. Poster presented at the 2nd MAE Graduate Research Symposium, Oklahoma State University, Stillwater, OK, November 2, 2018

56. Ahmed, M., San, O., and Bach, C. Air mixer design guidelines using CFD analysis (ASHRAE,RP-1733). Poster presented at the 2nd MAE Graduate Research Symposium, Oklahoma State University, Stillwater, OK, November 2, 2018

57. Rahman, S. M. and San, O. An optimized discrete filtering framework for large eddy simulations. 38th Oklahoma AIAA/ASME Symposium, Oklahoma Christian University, Oklahoma City, OK, April 14, 2018

58. Maulik, R., Ozbenli, E., San, O., and Vedula, P. Sub-filter scale nonlinear interaction modeling for LES using deep learning networks. 38th Oklahoma AIAA/ASME Symposium, Oklahoma Christian University, Oklahoma City, OK, April 14, 2018

59. Nelsen, N. and San, O. Reduced order framework for optimal control of nonlinear PDE. Poster presented at the Coalition for Advancing Digital Research & Education (CADRE) conference, Oklahoma State University, Stillwater, OK, April 17–18, 2018

60. Maulik, R., Ozbenli, E., San, O., and Vedula, P. Nonlinear physics inference using artificial neural networks. Poster presented at the Coalition for Advancing Digital Research & Education (CADRE) conference, Oklahoma State University, Stillwater, OK, April 17–18, 2018

61. Rahman, S. M. and San, O. MPI implementation of Navier–Stokes equations. Poster presented at the Coalition for Advancing Digital Research & Education (CADRE) conference, Oklahoma State University, Stillwater, OK, April 17–18, 2018

62. Kholikov, K., Seyitliyev, D., Grant, B., San, O., and Er, A. One-step laser-induced hydrogen generation from graphite and coal in water. APS March Meeting 2018, Los Angeles, CA, March5–9, 2018

63. Staples, A. E. and San, O. A coarse grid projection multiscale method for turbulent geophysical flows. International Workshop on Complex Turbulent Flows, Tangier, Morocco, November 27–28, 2017

64. Maulik, R. and San, O. A neural network approach for the blind deconvolution of turbulent flows. 70th Annual Meeting of the APS Division of Fluid Dynamics, Denver, CO, November19–21, 2017

65. Kholikov, K., Seyitliyev, D., Grant, B., San, O., and Er, A. Laser-induced hydrogen generation from graphite and coal. 84th Annual Meeting of the APS Southeastern Section, Milledgeville, GA, November 16–19, 2017

66. Maulik, R. and San, O. Subfilter recovery for the turbulence modeling of large eddy simulations using sparse regression for blind deconvolution. Poster presented at the MAE Graduate Research Symposium, Oklahoma State University, Stillwater, OK, October 28, 2017

67. Ahmed, M., San, O., and Bach, C. CFD analysis of HVAC duct design and fittings. MAE Graduate Research Symposium, Oklahoma State University, Stillwater, OK, October 28, 2017

68. Maulik, R. and San, O. Implicit LES modeling of stratified shear layer turbulence. Poster presented at the Coalition for Advancing Digital Research & Education (CADRE) conference, Oklahoma State University, Stillwater, OK, April 11–12, 2017

69. Maulik, R., San, O., and R., B. An implicit large eddy simulation framework based on wavelet adaptive mesh refinement. 37th Oklahoma AIAA/ASME Symposium, Oral Roberts University, Tulsa, OK, April 15, 2017

70. Ahmed, M. and San, O. Artificial neural network models for model order reduction of nonlinear systems. 37th Oklahoma AIAA/ASME Symposium, Oral Roberts University, Tulsa, OK, April15, 2017

71. Glenn, W. and San, O. Constitutive modeling in reduced order representation of hemodynamic flows. 37th Oklahoma AIAA/ASME Symposium, Oral Roberts University, Tulsa, OK, April15, 2017

72. San, O. and Ahmed, M. An artificial neural network closure modeling framework for model order reduction of convective flows. SIAM Conference on Computational Science and Engineering (CSE17), Atlanta, GA, February 27–March 3, 2017

73. San, O. and Maulik, R. A dynamic framework for subgrid-scale parametrization of mesoscale eddies in geophysical flows.  69th Annual Meeting of the APS Division of Fluid Dynamics, Portland, OR, November 20–22, 2016

74. Maulik, R. and San, O. A dynamic hybrid subgrid-scale modeling framework for large eddy simulations.  69th Annual Meeting of the APS Division of Fluid Dynamics, Portland, OR, November 20–22, 2016

75. Maulik, R. and San, O. An explicit filtering framework based on Perona-Malik anisotropic diffusion for shock capturing and subgrid scale modeling of Burgers’ turbulence. 69th Annual Meeting of the APS Division of Fluid Dynamics, Portland, OR, November 20–22, 2016

76. Maulik, R. and San, O. Evaluation of explicit and implicit LES closures for Burgers turbulence. 36th Oklahoma AIAA/ASME Symposium, University of Oklahoma, Norman, April 16, 2016

77. San, O., Behera, R., Grenga, T., Matous, K., and Paolucci, S. WAMR: An accurate predictive tool. Poster presented at the Center for Shock Wave-processing of Advanced Reactive Materials, University of Notre Dame, Notre Dame, IN, November 5–6, 2014

78. Staples, A. E. and San, O. An efficient coarse grid projection method for quasi-geostrophic models of large-scale ocean circulation.  66th Annual Meeting of the APS Division of Fluid Dynamics, Pittsburgh, PA, November 24–26, 2013

79. Iliescu, T., San, O., Wang, Z., Foster, E., and Staples A. E. Large eddy simulation of the quasi-geostrophic equations of oceanic flows. American Geophysical Union Fall Meeting, San Francisco, CA, December 3–7, 2012

80. Staples, A. E. and San, O. A posteriori analysis of spatial filters for approximate deconvolution large eddy simulations of homogeneous incompressible flows. 65th Annual Meeting of the APS Division of Fluid Dynamics, San Diego, CA, November 18–20, 2012

81. San, O. and Staples, A. E. A coarse-grid projection method for accelerating incompressible flow computations. 64th Annual Meeting of the APS Division of Fluid Dynamics, Baltimore, MD, November 20–22, 2011

82. Staples, A. E. and San, O. Approximate deconvolution large eddy simulation of a barotropic ocean circulation model. 64th Annual Meeting of the APS Division of Fluid Dynamics, Baltimore, MD, November 20–22, 2011

83. San, O. and Staples, A. E. A coarse-grid projection multiscale method for accelerating incompressible flow computations. Fall Fluid Mechanics Symposium, Virginia Tech, Blacksburg, VA, November 3, 2011

84. San, O. and Staples, A. E. Dynamics of pulsatile flows through elastic microtubes. 63rd Annual Meeting of the APS Division of Fluid Dynamics, Long Beach, CA, November 21–23, 2010

85. San, O. and Staples, A. E. A global reduced-order distributed model for physiological fluid dynamics. Biomedical Engineering Society Annual Meeting, Austin, TX, October 6–9, 2010

86. San, O. and Staples, A. E. An algorithm for a reduced-order representation of physiological fluid dynamics. VT Symposium on Reduced-Order Modeling and System Identification, Blacksburg, VA, February 15, 2010

87. San, O. and Staples, A. E. A multigrid accelerated high-order compact fractional step method for unsteady incompressible viscous flows. 62nd Annual Meeting of the APS Division of Fluid Dynamics, Minneapolis, MN, November 22–24, 2009

88. Staples, A. E. and San, O. An efficient algorithm for computing physiological fluid flows. 62nd Annual Meeting of the APS Division of Fluid Dynamics, Minneapolis, MN, November 22–24,2009

89. San, O. and Staples, A. E. An efficient model and algorithm for physiological fluid dynamics. Joint ASCE-ASME-SES Conference on Mechanics and Materials, Blacksburg, VA, June 24–27, 2009

90. San, O. and Staples, A. E. Multiscale modeling of physiological fluid dynamics. 25th Annual Research Symposium and Exposition, Virginia Tech, Blacksburg, VA, March 25, 2009

91. San, O., Yalcin, S. E., and Baysal, O. Numerical simulation of a low voltage electrokinetic micropump for microfluidic LOC devices. AIAA Region I-MA Student Conference, University of Maryland, College Park, MD, April 11, 2008

92. San, O. and Baysal, O.  Numerical simulations of MEMS-based micropumps.  5th Annual Research Exposition, Old Dominion University, Norfolk, VA, April 9, 2008

93. San, O. and Bayraktar, I. Numerical modeling of gas flow in converging-diverging micronozzles. 37th AIAA Fluid Dynamics Conference and Exhibit, Miami, FL, June 25–28 2007

94. San, O. and Bayraktar, I.  Assessment of slip wall conditioning for gas flow simulation in micronozzles. 18th AIAA Computational Fluid Dynamics Conference, Miami, FL, June 25–28, 2007

95. San, O. Numerical analysis of gas flow in micronozzles. AIAA Young Professional, Student and Education Conference, Johns Hopkins University, Baltimore, MD, November 10–11, 2006

96. San, O. 2D unsteady incompressible Navier–Stokes solution of cavity flow. AIAA Region I-MA Student Conference, Penn State, University Park, PA, April 7–8, 2006