AI RESEARCH
A Critical Assessment of PINNs and Operator Learning for Geotechnical Engineering
arXiv CS.LG
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ArXi:2512.24365v2 Announce Type: replace-cross Scientific machine learning (SciML) offers neural-network alternatives to numerical workflows in geotechnical engineering. This paper benchmarks multi-layer perceptrons (MLPs), physics-informed neural networks (PINNs), deep operator networks (DeepONet), and graph network simulators (GNS) against finite-difference and particle-based references on geotechnical benchmarks, and compares PINN inversion with automatic differentiation (AD) through a conventional solver. We evaluate each method for extrapolation.