Introduces structured DRO for learned inverse problem reconstructions with ambiguity sets aligned to the forward operator, yielding explicit dual representations and a worst-case bound that induces Tikhonov regularization on the operator Lipschitz constant.
Nathan and Brunton, Bingni W
5 Pith papers cite this work, alongside 273 external citations. Polarity classification is still indexing.
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273
external citations · external index
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An active learning method based on E-SINDy identifies governing ODEs and PDEs accurately with significantly fewer data samples than random sampling across tested systems.
Proposes adaptive multiple importance sampling for robust Bayesian model evidence estimation under parameter non-identifiability, shown to outperform deterministic methods on ecological case studies while being cheaper than MCMC.
AutoSINDy automatically builds a tailored basis library from PySR symbolic regression and applies SINDy to recover ground-truth nonlinear dynamics with 92.8% success under noise.
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.
citing papers explorer
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A Distributionally Robust Framework for Learned Reconstructions in Inverse Problems
Introduces structured DRO for learned inverse problem reconstructions with ambiguity sets aligned to the forward operator, yielding explicit dual representations and a worst-case bound that induces Tikhonov regularization on the operator Lipschitz constant.
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How Low Can You Go? Active Learning for Sparse Model Discovery in the Ultra-Low-Data Limit
An active learning method based on E-SINDy identifies governing ODEs and PDEs accurately with significantly fewer data samples than random sampling across tested systems.
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Reliable model selection in the presence of parameter non-identifiability
Proposes adaptive multiple importance sampling for robust Bayesian model evidence estimation under parameter non-identifiability, shown to outperform deterministic methods on ecological case studies while being cheaper than MCMC.
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Discovery of Nonlinear Dynamics with Automated Basis Function Generation
AutoSINDy automatically builds a tailored basis library from PySR symbolic regression and applies SINDy to recover ground-truth nonlinear dynamics with 92.8% success under noise.
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Data-Driven Equation Discovery for Nonlinear Liquid Film Flows
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.