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