# Links

“You need to be aware of what others are doing, applaud their efforts, acknowledge their successes, and encourage them in their pursuits. When we all help one another, everybody wins.”

― Jim Stovall

## Resources

Artificial Intelligence and Scientific Computing for Fluid Mechanics by Petros Koumoutsakos

Digital twins: A personalized future of computing for complex systems | Karen Willcox | TEDxUTAustin

Data-driven Physical Simulations (DDPS) Seminar Series [YouTube Channel]

Large Eddy Simulation Reduced Order Models (Prof. Traian Iliescu)

Machine Learning for Reduced-Order Modeling (Prof. Bernd R. Noack)

Danielle Maddix Robinson: Physics-constrained machine learning for scientific computing

MIT 6.S191 (2019): Introduction to Deep Learning by Alexander Amini and Ava Soleimany

Advice for and Expectations from New Students by Prof. Christos Kozyrakis (and Prof. Jan Hesthaven)

Own your PhD project: How to take charge of your research, by Niki Kringos

Essential PhD tips: 10 articles all doctoral students should read

How to write a Great Research Paper, and Get it Accepted by a Good Journal, by Anthony Newman

Designing effective scientific presentations, by Susan McConnell

Lecture notes on Numerical Linear Algebra, by Joseph E. Flaherty

Fundamentals of Engineering Numerical Analysis, by Prof. Parviz Moin

CFD Vision 2030 Study: A Path to Revolutionary Computational Aerosciences

Compressive Sensing and Sparse Recovery Lecture, by Prof. Justin Romberg

Compressed Sensing: Recovery, Algorithms, and Analysis, by Prof. Stanley Osher

Compressed Sensing and Dynamic Mode Decomposition, by Prof. Steve Brunton

Advanced High Performance Computing CSCI 580, by Dr. Timothy H. Kaiser

How do you combine machine learning and physics-based modeling? by V. Flovik

The mostly complete chart of Neural Networks, explained by Andrew Tchircoff

THE NEURAL NETWORK ZOO, by Fjodor van Veen (with links to the original papers)

Argonne Training Program on Extreme-Scale Computing (2016) or here

Stefano Marelli: Metamodels for uncertainty quantification and reliability analysis

Essentials of Atmospheric and Oceanic Dynamics, by Prof. Geoffrey K. Vallis

pyMOR & SIAM J. SCI. COMPUT. 2016 Vol. 38, No. 5, pp. S194–S216

Max Gunzburger: Uncertainty Quantification for Complex Systems

Jeremy Oakley: Introduction to Uncertainty Quantification and Gaussian Processes - GPSS 2016

Fast Quantification of Uncertainty and Robustness with Variational Bayes by Tamara Broderick

Emily Gorcenski - Polynomial Chaos: A technique for modeling uncertainty

Artificial Intelligence, the History and Future, by Chris Bishop

Chris Fonnesbeck: An introduction to Markov Chain Monte Carlo using PyMC3

Metropolis-Hastings, the Gibbs Sampler, and MCMC by Dr. Esarey

Paul Balzer - IPython and Sympy to Develop a Kalman Filter for Multisensor Data Fusion

Building an ocean model from scratch | Week 13 | MIT 18.S191 Fall 2020 | Henri Drake

Samuli Siltanen: Reconstruction methods for ill-posed inverse problems - Part 1

Samuli Siltanen: Reconstruction methods for ill-posed inverse problems - Part 2

## Graduate Fellowship Opportunities

## Summer Schools and Postdoctoral Research & Research Centers

## Lecture Series

Mathematical Methods for Engineers I, by Prof. Gilbert Strang, MIT

Mathematical Methods for Engineers II, by Prof. Gilbert Strang, MIT

Introduction to Continuum Mechanics, by Prof. Romesh Batra, VT

Instability and Transition of Fluid Flows, by Prof. Tapan K. Sengupta

Dynamic Data Assimilation: an introduction by Prof S. Lakshmivarahan

Introduction to Computer Science and Programming, by Profs. E. Grimson and J. Guttag

Probabilistic Systems Analysis and Applied Probability, by John Tsitsiklis

2.160 Identification, Estimation, and Learning, by Prof. Harry Asada