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Found 4 papers, 4 papers with code
Port-metriplectic neural networks: thermodynamics-informed machine learning of complex physical systems
We show that the constructed networks are able to learn the physics of complex systems by parts, thus alleviating the burden associated to the experimental characterization and posterior learning process of this kind of systems.
Thermodynamics-informed neural networks for physically realistic mixed reality
The imminent impact of immersive technologies in society urges for active research in real-time and interactive physics simulation for virtual worlds to be realistic.
Thermodynamics-informed graph neural networks
In this paper we present a deep learning method to predict the temporal evolution of dissipative dynamic systems.
Structure-preserving neural networks
The method employs a minimum amount of data by enforcing the metriplectic structure of dissipative Hamiltonian systems in the form of the so-called General Equation for the Non-Equilibrium Reversible-Irreversible Coupling, GENERIC [M. Grmela and H. C Oettinger (1997).
