NUMRAD24
The 2024 edition of NUMRAD took place on June 11–14, 2024, bringing together leading researchers in numerical analysis, stochastic modeling, and machine learning. Younger researchers had the opportunity to present their work during two poster sessions.
Core topics included:
- Random & Stochastic Differential Equations
- Optimisation under Uncertainty
- Numerical Methods for Random PDEs
- Scientific Machine Learning and Neural Operators
More specifically, we had the pleasure to hear from the following experts
- Harbir Antil (George Mason University, USA) on Digital Twins and Optimisation Under Uncertainty — Compression and Decomposition
- Albert Cohen (Sorbonne Université, France) on Optimal linear and non-linear dimensionality reduction
- Caroline Geiersbach (Weierstrass Institute, Germany) on Stochastic approximation for PDE-constrained optimization under uncertainty
- Siddhartha Mishra (ETH Zürich, Switzerland) on Learning Solutions of PDEs
- Fabio Nobile (EPF Lausanne, Switzerland) on Numerical approximation of random partial differential equations
- Michela Ottobre (Heriot Watt University, United Kingdom) on Uniform in time (numerical) approximations of Stochastic Differential Equations
- Thomas M. Surowiec (Simula Research Laboratory, Norway) on An Introduction to Optimization under Uncertainty
- Aretha Teckentrup (University of Edinburgh, United Kingdom) on Multilevel Monte Carlo methods for random partial differential equations
- Gilles Vilmart (Université de Genève, Switzerland) on Stiff integrators for stochastic (partial) differential equations
- Jakob Zech (Heidelberg University, Germany) on Neural Networks for UQ
This summer school was organized with the generous support of CSQI and EMS.
Organizers: Matteo Raviola, Thomas Trigo Trindade, Tommaso Vanzan, Fabio Zoccolan