Talks and Tutorials
- Tutorials presented by RTG members at the SAMSI transition workshop on Numerical Analysis in Data Science
- Randomized Algorithms for Least Squares Problems, by Ilse Ipsen
- A biased Introduction to Sensitivity Analysis, by Pierre Gremaud
- Optimal Design of Experiments for Large-scale Bayesian Inverse Problems, by Alen Alexanderian
- Bayesian Inference and Uncertainty Propagation for Physical and Biological Models, by Ralph Smith
- Ties between Sensitivity Analysis, Active Subspaces, Identifiability Analysis, and Parameter Subset Selection, by Ralph Smith
- Anderson Acceleration: Convergence Theory and Numerical Experience, by Tim Kelley
- HDSA for Bayesian Inverse Problems, by Issac Sunseri
- Randomized approaches to accelerate MCMC algorithms for Bayesian Inverse Problems, by Arvind Saibaba
- Global sensitivity driven input dimension reduction for reaxff parameterization of silica-based glasses, by Helen Cleaves
- Probabilistic Roundoff Error Analysis, by Johnathan Rhyne
- Phase Separation and Volume Expansion in Lithium-Ion Batteries, by Sreeram Venkat
- Rank Revealing QR Factorizations, by Benjamin Daniel
- Multi-Level Monte Carlo Polynomial Chaos, by Mike Merritt
- Probabilistic Roundoff Error Analysis for Sums, by Eric Hallman
- Multi-Level Monte Carlo, by Devon Troester and Eric Hallman
- Accelerated Gradient Optimization: A Multiscale Analysis, by Mohammad Farazmand
- Probabilistic Numerical Linear Solvers, by Ilse Ipsen (recorded talk)
- On marginals of Gaussian random vectors, by Isaac Sunseri and Alen Alexanderian
- Computing gradients and Hessians using the adjoint method, by Alen Alexanderian and Isaac Sunseri
- Computer tutorial on global sensitivity analysis, by Alen Alexanderian, Pierre Gremaud, and Ralph Smith
Computer codes for global sensitivity analysis: Matlab code
You can also view these codes on GitHub