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We develop a method to learn physical systems from data that employs feedforward neural networks and whose predictions comply with the first and second principles of thermodynamics.
Autoencoder-based incremental class learning without retraining on old data
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Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups
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Data-driven computational mechanics
Generic guide to the multiscale dynamics and thermodynamics
Miroslav Grmela · 2018
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A manifold learning approach to data-driven computational elasticity and inelasticity
Rubén Ibanez, Emmanuelle Abisset-Chavanne, Jose Vicente Aguado, David Gonzalez, Elias Cueto, and Francisco Chinesta · 2018
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On learning hamiltonian systems from data
Tom Bertalan, Felix Dietrich, Igor Mezić, and Ioannis G. Kevrekidis · 2019
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Approximation by superpositions of a sigmoidal function
Euntae Choi and Kyungmi Lee and Kiyoung Choi · 2019
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Data-driven generic modeling of poroviscoelastic materials
Chady Ghnatios, Iciar Alfaro, David González, Francisco Chinesta, and Elias Cueto · 2019
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A proposal on machine learning via dynamical systems
Weinan E · 2017
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Data-driven non-linear elasticity: constitutive manifold construction and problem discretization
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Thermodynamically consistent data-driven computational mechanics
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Gradient and generic evolution towards reduced dynamics, 2019
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Hybrid constitutive modeling: data-driven learning of corrections to plasticity models
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Interpretable machine learning: definitions, methods, and applications
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Pytorch: An imperative style, high-performance deep learning library
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Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
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