GFINNs: GENERIC formalism informed neural networks for deterministic and stochastic dynamical systems

  title={GFINNs: GENERIC formalism informed neural networks for deterministic and stochastic dynamical systems},
  author={Zhen Zhang and Yeonjong Shin and George Em Karniadakis},
  journal={Philosophical Transactions of the Royal Society A},
We propose the GENERIC formalism informed neural networks (GFINNs) that obey the symmetric degeneracy conditions of the GENERIC formalism. GFINNs comprise two modules, each of which contains two components. We model each component using a neural network whose architecture is designed to satisfy the required conditions. The component-wise architecture design provides flexible ways of leveraging available physics information into neural networks. We prove theoretically that GFINNs are… 

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