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Found 2 papers, 1 papers with code
Probabilistic solution of chaotic dynamical system inverse problems using Bayesian Artificial Neural Networks
Bayesian Artificial Neural Networks can be used to simultaneously fit a model and estimate model parameter uncertainty.
Model inference for Ordinary Differential Equations by parametric polynomial kernel regression
This work introduces a parametric polynomial kernel method that can be used for inferring the future behaviour of Ordinary Differential Equation models, including chaotic dynamical systems, from observations.
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