Learning non-Markovian physics from data
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Publication:2128336
DOI10.1016/j.jcp.2020.109982OpenAlexW3097023501MaRDI QIDQ2128336
Publication date: 21 April 2022
Published in: Journal of Computational Physics (Search for Journal in Brave)
Full work available at URL: http://hdl.handle.net/10985/19926
machine learningGENERICgeneralized Langevin equationMori-Zwanzig projectionhistory-dependent physics
Related Items (4)
Extension of dynamic mode decomposition for dynamic systems with incomplete information based on t-model of optimal prediction ⋮ Port-metriplectic neural networks: thermodynamics-informed machine learning of complex physical systems ⋮ Modular machine learning-based elastoplasticity: generalization in the context of limited data ⋮ Material Modeling via Thermodynamics-Based Artificial Neural Networks
Uses Software
Cites Work
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