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Author

Prashant K. Jha

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#machine learning Preprint Sep 2026

Adapting neural operators for mechanics decisions under changing operating conditions

Neural operators can accelerate repeated nonlinear mechanics calculations, but their accuracy can deteriorate as operating conditions move beyond the training range. This work studies whether high-fidelity solutions acquired during use can be reused to adapt a neural operator and improve subsequent mechanics-based comm...

Prashant K. Jha, K. Enakoutsa, Ian Galloway et al. · 0 citations
Review Open access Mar 2025

From Theory to Application: A Practical Introduction to Neural Operators in Scientific Computing

This review examines neural operator architectures for learning solution operators of parametric partial differential equations (PDEs), with an emphasis on conceptual clarity and practical implementation, and positioning neural operators within broader scientific-computing workflows and by identifying directions for re...

Prashant K. Jha · 8 citations

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