Adaptivity, High Dimensionality and Randomness

The overall goal of this workshop is to initiate new and groundbreaking research in algorithm design and mathematical analysis in scientific computing. The focus is directed at the numerical approximation of partial differential equations (PDEs for short), when randomness is involved either in the input data of the modeling equations or in the approximation algorithms themselves.

Rather than a tight focus on a highly specialized, clearly delineated research field, the workshop aims at cross-fertilization of several recently emerged research directions in applied and computational mathematics around the numerical solution of PDEs. This comprises, in particular, mathematically justified paradigms for the design of adaptive algorithms, the recently strongly advanced understanding of randomized and number-theoretic algorithms such as Monte-Carlo (MC), quasi-Monte Carlo (QMC) and Markov Chain MC (MCMC), sparse and high dimensional approximation, tensor-formatted computations, deep neural network approximation and machine learning algorithms.

Coming soon.

  • Michael Feischl (TU Vienna) — Organizer
At a glance
April 4, 2022 — April 8, 2022
ESI Boltzmann Lecture Hall
Carsten Carstensen (HU Berlin)
Albert Cohen (Sorbonne U, Paris)
Michael Feischl (TU Vienna)
Christoph Schwab (ETH Z├╝rich)