A push-forward stochastic inversion framework with sparsity priors is applied to discover governing equations and coefficient distributions from noisy, variable dynamical system data.
Data-consistent inversion for stochastic input-to- output maps,
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Discovering Governing Equations in the Presence of Uncertainty
A push-forward stochastic inversion framework with sparsity priors is applied to discover governing equations and coefficient distributions from noisy, variable dynamical system data.