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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…
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-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…
Beyond black-boxes in Bayesian inverse problems and model validation: applications in solid mechanics of elastography
Lukas Bruder, Phaedon-Stelios Koutsourelakis
The present paper is motivated by one of the most fundamental challenges in inverse problems, that of quantifying model discrepancies and errors. While significant strides have bee…
Physics-constrained, data-driven discovery of coarse-grained dynamics
L. Felsberger, P. S. Koutsourelakis
The combination of high-dimensionality and disparity of time scales encountered in many problems in computational physics has motivated the development of coarse-grained (CG) model…