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Bayesian polynomial neural networks and polynomial neural ordinary differential equations
We develop and validate Bayesian inference methods for obtaining Bayesian uncertainties for the parameters in symbolic neural Ordinary Differential Equations.
Colby Fronk, Jaewoong Yun, Prashant Singh, Linda Petzold
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Bayesian polynomial neural networks and polynomial neural ordinary differential equations
Interpretable polynomial neural ordinary differential equations
We introduce the polynomial neural ODE, which is a deep polynomial neural network inside of the neural ODE framework. We demonstrate the capability of polynomial neural ODEs to predict outside of the training region, as well as to perform direct symbolic regression without using additional tools such as SINDy.
Colby Fronk, Linda Petzold
Cite DOI
Interpretable polynomial neural ordinary differential equations