BINODEs combine known stoichiometric matrices with neural network processes to learn and recover dynamics in biochemical systems while incorporating biological constraints.
Extreme theory of functional connections: A fast physics-informed neural network method for solving ordinary and partial differential equations
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VMLFN converts physics-informed neural network training into a linear matrix solve using variational weak forms for efficient parametric multiphysics surrogates.
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Learning dynamical systems with biochemically informed neural ordinary differential equations
BINODEs combine known stoichiometric matrices with neural network processes to learn and recover dynamics in biochemical systems while incorporating biological constraints.
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Variational Matrix-Learning Fourier Networks for Parametric Multiphysics Surrogates
VMLFN converts physics-informed neural network training into a linear matrix solve using variational weak forms for efficient parametric multiphysics surrogates.