no code implementations • 11 Jan 2024 • Constatin Gahr, Ionut-Gabriel Farcas, Frank Jenko
We first use the data obtained via a direct numerical simulation of the HW equations starting from a specific initial condition and train OpInf ROMs for predictions beyond the training time horizon.
no code implementations • 28 Sep 2023 • Robin Greif, Frank Jenko, Nils Thuerey
Turbulence in fluids, gases, and plasmas remains an open problem of both practical and fundamental importance.
1 code implementation • 20 Nov 2022 • Ionut-Gabriel Farcas, Benjamin Peherstorfer, Tobias Neckel, Frank Jenko, Hans-Joachim Bungartz
When training low-fidelity models, the proposed approach takes into account the context in which the learned low-fidelity models will be used, namely for variance reduction in Monte Carlo estimation, which allows it to find optimal trade-offs between training and sampling to minimize upper bounds of the mean-squared errors of the estimators for given computational budgets.
no code implementations • 2 May 2022 • Maximilian Mueller, Robin Greif, Frank Jenko, Nils Thuerey
We investigate uncertainty estimation and multimodality via the non-deterministic predictions of Bayesian neural networks (BNNs) in fluid simulations.