Computational Uncertainty Quantification: Mathematical Foundations, Methodology & Data
This ESI TP gathers at ESI leading researchers from applied mathematics, scientific computing and high-dimensional, computational statistics.
Between May 2nd and June 24th The Erwin Schrödinger International Institute for Mathematics and Physics (ESI) gathered the leading researchers from applied mathematics, scientific computing and high-dimensional, computational statistics around the emerging area of numerical uncertainty quantification (UQ for short) in engineering and in the sciences. The Thematic Programme concentrated on mathematical foundations and underpinnings of novel computational strategies for the efficient numerical approximation of partial differential equations (PDEs) with uncertain inputs, as well as on the analysis of statistical methodologies for high-dimensional statistical data resulting from such PDE simulations. Both forward and inverse problems were considered.
This workshop covered multilevel and multifidelity sampling methods in uncertainty quantification for PDEs, with a particular focus on moving beyond forward propagation of uncertainty.
Clemens Heitzinger was part of the organziers team and presented on the 3rd of June “Convergence in Distributional Reinforcement Learning and Applications”.
Further information see here https://www.esi.ac.at/events/e22/.