Orthogonal Discrepancy Kernels for Learning with Partial Physics
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Abstract (arXiv)
We introduce a semi-parametric framework for nonlinear system identification, which decouples discrepancy functions from physics-based components. Orthogonal Gaussian process regression balances sparse parameter selection (the white box) with discrepancy learning (the black box) to produce interpretable models from incomplete physics.
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References · 5
- 2020Weak SINDy For Partial Differential Equationscited by 155
- 2020On spike-and-slab priors for Bayesian equation discovery of nonlinear dynamical systems via sparse linear regressioncited by 24
- 2020Sparse Identification of Nonlinear Dynamical Systems via Reweighted ℓ1-regularized Least Squarescited by 21
- 2023A spectrum of physics-informed Gaussian processes for regression in engineeringcited by 14
- 2016Orthogonal Gaussian process modelscited by 10
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