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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6 Pith papers cite this work, alongside 11 external citations. Polarity classification is still indexing.
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Using occultation-derived sizes and Herschel thermal data, the study identifies three previously unknown likely binary TNOs and reports satellite size estimates while confirming no sizable satellites are needed for three others.
Replacing an irregular body by an equal-mass binary dumbbell converts its spin-orbit resonances into surrogate mean-motion resonances; for Quaoar the resulting maps show SORs play no relevant role in present ring dynamics.
BG-SINDy reformulates l0-constrained regression as term-level l2,0 regularization and uses progressive pruning guided by balance contributions to recover small-coefficient terms in multiscale PDEs.
Bayesian optimization with Gaussian process surrogate accelerates numerical calibration of Mølmer-Sørensen gate parameters, with performance tied to quantum projection 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.
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Active Learning for Calibrating Entangling Gates via Surrogate-Based Optimization
Bayesian optimization with Gaussian process surrogate accelerates numerical calibration of Mølmer-Sørensen gate parameters, with performance tied to quantum projection noise.