Original paper

Manifold learning for coarse-graining atomistic simulations: Application to amorphous solids

Volume: 215, Pages: 117008 - 117008
Published: Aug 1, 2021
Abstract
We introduce a generalized machine learning framework to probabilistically parameterize upper-scale models in the form of nonlinear PDEs consistent with a continuum theory, based on coarse-grained atomistic simulation data of mechanical deformation and flow processes. The proposed framework utilizes a hypothesized coarse-graining methodology with manifold learning and surrogate-based optimization techniques. Coarse-grained high-dimensional data...
Paper Details
Title
Manifold learning for coarse-graining atomistic simulations: Application to amorphous solids
Published Date
Aug 1, 2021
Volume
215
Pages
117008 - 117008
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