PAS method estimates extreme non-linear wave impact loads within 2-15% of brute-force Monte Carlo results while using only 1-3% of the high-fidelity simulation time.
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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.
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Design loads for wave impacts -- introducing the Probabilistic Adaptive Screening (PAS) method for predicting extreme non-linear loads on maritime structures
PAS method estimates extreme non-linear wave impact loads within 2-15% of brute-force Monte Carlo results while using only 1-3% of the high-fidelity simulation time.
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Balance-Guided Sparse Identification of Multiscale Nonlinear PDEs with Small-coefficient Terms
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.