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Predictive Collective Variable Discovery with Deep Bayesian Models
Markus Schöberl, Nicholas Zabaras, Phaedon-Stelios Koutsourelakis
Extending spatio-temporal scale limitations of models for complex atomistic systems considered in biochemistry and materials science necessitates the development of enhanced sampli…
A data-driven model order reduction approach for Stokes flow through random porous media
Constantin Grigo, Phaedon-Stelios Koutsourelakis
Direct numerical simulation of Stokes flow through an impermeable, rigid body matrix by finite elements requires meshes fine enough to resolve the pore-size scale and is thus a com…
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…