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Probabilistic data fusion and physics-informed machine learning: A new paradigm for modeling and computation under uncertainty
Presenter
- Paris Perdikaris
September 28, 2018
IPAM
Scalable algorithms for optimal experimental design for large-scale Bayesian inverse problems governed by complex models
Presenter
- Omar Ghattas
October 18, 2018
IPAM
Scalable algorithms for optimal training data for Bayesian inference of large scale models
Presenter
- Omar Ghattas
September 27, 2018
IPAM
Stan Osher - A Characteristic-Based Deep Learning Framework for Hamilton–Jacobi Equations with Application to Optimal Transport
Presenter
- Stanley Osher
July 14, 2025
IPAM
Computing Lusztig's q-weight multiplicities beyond type A
Presenter
- Cristian Lenart
November 10, 2025
ICERM
Designing protein models with machine learning and experimental data
Presenter
- Klara Bonneau
April 26, 2024
IMSI
Parametrizing coarse-grained molecular systems from ab-initio computations: some elements
Presenter
- Gabriel Stoltz
October 24, 2016
IPAM
Generating Representative Point Samples from Probability Distributions
Presenter
- Nathan Kirk
March 31, 2025
IMSI
Quantitative Propagation of Chaos for 2D Viscous Vortex Model on the Whole Space
Presenter
- Zhenfu Wang
May 6, 2024
ICERM