1.1k citations · 1.1k across the 7 of their papers we have counts for
14 papers
Multi-fidelity Constrained Optimization for Stochastic Black Box Simulators
Atul Agrawal, Kislaya Ravi, Phaedon-Stelios Koutsourelakis +1
Constrained optimization of the parameters of a simulator plays a crucial role in a design process. These problems become challenging when the simulator is stochastic, computationa…
Physics-Informed Tensor Basis Neural Network for Turbulence Closure Modeling
Leon Riccius, Atul Agrawal, Phaedon-Stelios Koutsourelakis
Despite the increasing availability of high-performance computational resources, Reynolds-Averaged Navier-Stokes (RANS) simulations remain the workhorse for the analysis of turbule…
Self-supervised optimization of random material microstructures in the small-data regime
Maximilian Rixner, Phaedon-Stelios Koutsourelakis
While the forward and backward modeling of the process-structure-property chain has received a lot of attention from the materials community, fewer efforts have taken into consider…
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…
A probabilistic generative model for semi-supervised training of coarse-grained surrogates and enforcing physical constraints through virtual observables
Maximilian Rixner, Phaedon-Stelios Koutsourelakis
The data-centric construction of inexpensive surrogates for fine-grained, physical models has been at the forefront of computational physics due to its significant utility in many-…