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

dc.contributor.author	Takami, Tomoki
dc.contributor.author	Kitahara, Masaru
dc.date.accessioned	2026-08-20T21:25:31Z
dc.date.available	2026-08-20T21:25:31Z
dc.date.copyright	2025/11/25
dc.date.issued	2026
dc.identifier.other	AJRUA6.RUENG-1756.pdf
dc.identifier.uri	http://yetl.yabesh.ir/yetl1/handle/yetl/4314431
dc.description.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 simulation, this study employs an ...
dc.publisher	American Society of Civil Engineers
dc.title	Active Learning–Based Estimation of Extreme Roll Motion in Beam Seas with Supervised Dimensionality Reduction and Gradient Approximation
dc.type	Journal Article
dc.journal.volume	12
dc.journal.issue	1
dc.journal.title	ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering
dc.identifier.doi	10.1061/AJRUA6.RUENG-1756
dc.journal.fristpage	04025104-1
dc.journal.lastpage	04025104-11
dc.page	11
dc.tree	ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering:;2026:;Volume ( 012 ):;issue: 001
dc.contenttype	Fulltext