A recursive co-kriging multi-fidelity framework predicts wind-load coefficients for container ships in harbors more accurately than empirical models or single-fidelity surrogates while using fewer high-fidelity runs.
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A methodology for populational inverse problems that simultaneously deconvolves unknown observational noise and recovers parameter distributions via structured gradient descent and adaptive empirical measure-based active learning for surrogates.
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Predicting Wind Loads on Container Ships in Harbor Environments through Multi-Fidelity Modeling
A recursive co-kriging multi-fidelity framework predicts wind-load coefficients for container ships in harbors more accurately than empirical models or single-fidelity surrogates while using fewer high-fidelity runs.
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Efficient Deconvolution in Populational Inverse Problems
A methodology for populational inverse problems that simultaneously deconvolves unknown observational noise and recovers parameter distributions via structured gradient descent and adaptive empirical measure-based active learning for surrogates.