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Neural ordinary differential equations (NODEs) -- parametrizations of differential equations using neural networks -- have shown tremendous promise in learning models of unknown continuous-time dynamical systems from data.
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Neural networks with physics-informed architectures and constraints for dynamical systems modeling
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