Jun 1, 2021 · We presented a sparse-regression strategy for discovering coarse-grained equations from data generated with either a fine-scale model or Monte ...
Jan 30, 2020 · We replace the human discovery of such models, which typically involves spatial/stochastic averaging or coarse-graining, with a machine-learning ...
Feb 19, 2021 · We replace the human discovery of such models, which typically involves spatial/stochastic averaging or coarse-graining, with a machine-learning ...
Aug 31, 2023 · This thesis is centered around the application of machine learning techniques for uncovering hidden patterns and equations in complex physical systems.
May 23, 2022 · Abstract:We leverage data-driven model discovery methods to determine the governing equations for the emergent behavior of heterogeneous ...
Sep 6, 2023 · In this work, we leverage dimensionality reduction, sparse regression, and robust statistics to discover coarse-grained models of heterogeneous ...
Jul 12, 2022 · In this work, we propose a framework for discovering governing equations from noisy measurement data that is formulated via nonlinear dynamic ...
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We replace the human discovery of such models, which typically involves spatial/stochastic averaging or coarse-graining, with a machine-learning strategy based ...
In this paper, we advocate the paradigm of data-driven discovery for extracting governing equations by employing fine-scale simulation data. In particular, we ...
We leverage data-driven model discovery methods to determine governing equations for the emergent behavior of heterogeneous networked dynamical systems.