33 citations · 42 across the 7 of their papers we have counts for
7 papers
A fully GPU-based workflow for building physics emulators of hypersonic flows
Fabian Paischer, Dylan Rubini, Deniz A. Bezgin +6
The ability to resolve complex physical phenomena with high fidelity and at low computational cost is central to addressing key challenges in modern engineering. A prime example li…
Interpretable reduced-order modeling with time-scale separation
Sebastian Kaltenbach, Phaedon-Stelios Koutsourelakis, Petros Koumoutsakos
Partial Differential Equations (PDEs) with high dimensionality are commonly encountered in computational physics and engineering. However, finding solutions for these PDEs can be c…
Semi-supervised Invertible Neural Operators for Bayesian Inverse Problems
Sebastian Kaltenbach, Paris Perdikaris, Phaedon-Stelios Koutsourelakis
Neural Operators offer a powerful, data-driven tool for solving parametric PDEs as they can represent maps between infinite-dimensional function spaces. In this work, we employ phy…
Physics-enhanced Neural Networks in the Small Data Regime
Jonas Eichelsdörfer, Sebastian Kaltenbach, Phaedon-Stelios Koutsourelakis
Identifying the dynamics of physical systems requires a machine learning model that can assimilate observational data, but also incorporate the laws of physics. Neural Networks bas…
Physics-aware, deep probabilistic modeling of multiscale dynamics in the Small Data regime
Sebastian Kaltenbach, Phaedon-Stelios Koutsourelakis
The data-based discovery of effective, coarse-grained (CG) models of high-dimensional dynamical systems presents a unique challenge in computational physics and particularly in the…
Physics-aware, probabilistic model order reduction with guaranteed stability
Sebastian Kaltenbach, Phaedon-Stelios Koutsourelakis
Given (small amounts of) time-series' data from a high-dimensional, fine-grained, multiscale dynamical system, we propose a generative framework for learning an effective, lower-di…
Incorporating physical constraints in a deep probabilistic machine learning framework for coarse-graining dynamical systems
Sebastian Kaltenbach, Phaedon-Stelios Koutsourelakis
Data-based discovery of effective, coarse-grained (CG) models of high-dimensional dynamical systems presents a unique challenge in computational physics and particularly in the con…