AI RESEARCH
Operator Learning for Consolidation: An Architectural Comparison for DeepONet Variants
arXiv CS.LG
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ArXi:2507.10368v2 Announce Type: replace Deep Operator Networks (DeepONets) have emerged as a powerful surrogate modeling framework for learning solution operators in PDE-governed systems. While their use is expanding across engineering disciplines, applications in geotechnical engineering remain limited. This study systematically evaluates several DeepONet architectures for the consolidation problem.