A data-driven variational discretization of Onsager's principle learns uncertain free-energy and dissipation functionals from observations while guaranteeing provable energy stability for arbitrarily long simulations.
Alvarez Loya, D
2 Pith papers cite this work. Polarity classification is still indexing.
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Transformers trained on simulation data can predict atomistic transitions in nano-clusters while allowing generation of varied microstates and checks for physical validity.
citing papers explorer
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Flexible and Stable Dynamics Discovery with Onsager's Variational Principle
A data-driven variational discretization of Onsager's principle learns uncertain free-energy and dissipation functionals from observations while guaranteeing provable energy stability for arbitrarily long simulations.
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Predicting Atomistic Transitions with Transformers
Transformers trained on simulation data can predict atomistic transitions in nano-clusters while allowing generation of varied microstates and checks for physical validity.