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Published Subscription Review Article
Physics-Adaptive Digital Twin with Neural-Operator Reduced-Order ModellingBy Subash Ranjan Kabat, Bibhu Prasad Ganthia
Abstract: This study proposes a novel Physics-Adaptive Digital Twin with Neural-Operator Reduced-Order Modelling (PADT-NO) framework for predictive modelling of complex, nonlinear, and multiscale fluid flows. The proposed mathematical framework integrates fundamental conservation laws, Navier–Stokes dynamics, physics-constrained neural operators, adaptive reduced-order modelling, and uncertainty-aware state estimation within a unified computational architecture. Unlike conventional computational fluid dynamics and purely data-driven approaches, the proposed model dynamically couples high-fidelity physical information with a low-dimensional latent …
Published in Recent Trends in Fluid Mechanics · Vol. 13, Issue 2, 2026 · pp. 89–103 Read article →