A Riemannian stochastic representation for quantifying model uncertainties in molecular dynamics simulations

DOI10.1016/j.cma.2022.115702OpenAlexW4307259811WikidataQ115358824 ScholiaQ115358824MaRDI QIDQ2679453

Johann Guilleminot, Hao Zhang

Publication date: 20 January 2023

Published in: Computer Methods in Applied Mechanics and Engineering (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2207.13019

zbMATH Keywords

Stiefel manifoldmolecular dynamicsmodel uncertaintyuncertainty quantificationreduced-order modeling

Mathematics Subject Classification ID

Biology and other natural sciences (92-XX) Statistical mechanics, structure of matter (82-XX)

Related Items (2)

Probabilistic-learning-based stochastic surrogate model from small incomplete datasets for nonlinear dynamical systems ⋮ Representing model uncertainties in brittle fracture simulations

Uses Software

• LAMMPS
• Pi4U
• t-SNE
• ReaxFF
• UMAP

Cites Work

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