A semi-parametric framework decouples discrepancy functions from physics-based components via orthogonal Gaussian process regression for interpretable nonlinear system identification from incomplete physics.
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Data-driven equation discovery applied to liquid film flows identifies identifiability issues from multi-collinearity in monomial bases and early-time transients with large residuals.
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Orthogonal Discrepancy Kernels for Learning with Partial Physics
A semi-parametric framework decouples discrepancy functions from physics-based components via orthogonal Gaussian process regression for interpretable nonlinear system identification from incomplete physics.