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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…
A physics-aware, probabilistic machine learning framework for coarse-graining high-dimensional systems in the Small Data regime
Constantin Grigo, Phaedon-Stelios Koutsourelakis
The automated construction of coarse-grained models represents a pivotal component in computer simulation of physical systems and is a key enabler in various analysis and design ta…
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…