activity
20082026
most citedPhysics-Constrained Deep Learning for High-dimensional Surrogate Modeling and Uncertainty Quantification without Labeled Data

1.1k citations · 1.4k across the 43 of their papers we have counts for

collaborators
Showing 2019 · physics.comp-phShow all

2 papers · 2 filters

physics.comp-ph2019★ 33 cited

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…

physics.comp-ph2019★ 1.1k cited

Physics-Constrained Deep Learning for High-dimensional Surrogate Modeling and Uncertainty Quantification without Labeled Data

Yinhao Zhu, Nicholas Zabaras, Phaedon-Stelios Koutsourelakis +1

Surrogate modeling and uncertainty quantification tasks for PDE systems are most often considered as supervised learning problems where input and output data pairs are used for tra…