arXiv Machine Learning
FC-PINO: High Precision Physics-Informed Neural Operators via Fourier Continuation
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On the training of physics-informed neural operators for solving parametric partial differential equations
Physics-informed neural operators (PINOs) aim to learn solution operators for partial differential equations by using the governing physics as supervision, rather than relying solely on paired input-output simulation data. By incorporating physical constraints into the training objective, PINOs combine the cross-instance generalization of neural operators with the data efficiency of physics-informed learning.