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    Active Learning–Based Estimation of Extreme Roll Motion in Beam Seas with Supervised Dimensionality Reduction and Gradient Approximation 

    Source: ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering:;2026:;Volume ( 012 ):;issue: 001:;page 04025104-1
    Author(s): Takami, Tomoki; Kitahara, Masaru
    Publisher: American Society of Civil Engineers
    Abstract: AbstractAn efficient method is presented for estimating extreme nonlinear roll motion of a ship in beam seas in a given short-term sea state. With the aim of reducing the number of numerical simulations for roll motion ...
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    Uncertainty-Aware Efficient Estimation of the Probability Density Function for Excessive Rolling of a Ship 

    Source: ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering:;2025:;Volume ( 011 ):;issue: 004:;page 04025060-1
    Author(s): Takami, Tomoki; Kitahara, Masaru; Maki, Atsuo; Dostal, Leo
    Publisher: American Society of Civil Engineers
    Abstract: AbstractThis paper proposes a methodology to efficiently quantify an uncertainty in the probability density function (PDF) of excessive rolling of a ship. To this end, the Bayesian inference scheme is introduced to estimate ...
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