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Bayesian-Optimized Physics-Informed Deep Autoencoders for Efficient Calibration of Geotechnical Constitutive Models: A NorSand Case Study
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Indexed by:Journal Papers
Document Code:586193
Date of Publication:2025-12-01
Journal:ASCE-ASME JOURNAL OF RISK AND UNCERTAINTY IN ENGINEERING SYSTEMS PART A-CIVIL ENGINEERING
Volume:11
Issue:4
ISSN:2376-7642
Key Words:ARTIFICIAL NEURAL-NETWORKS
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