Learning Physics from an Imperfect Ancestor
Neural operators evaluate parametric partial differential equations cheaply but degrade sharply outside their training distribution. Physics-informed neural networks avoid dependence on labeled data, yet their optimization can be basin-fragile: when the governing residual admits multiple solutions, a PINN trained from scratch may converge to a physically incorrect state despite
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Neural operators evaluate parametric partial differential equations cheaply but degrade sharply outside their training distribution. Physics-informed neural networks avoid dependence on labeled data, yet their optimization can be basin-fragile: when the governing residual admits multiple solutions, a PINN trained from scratch may converge to a physically incorrect state despite
This paper addresses critical limitations of neural operators and physics-informed neural networks (PINNs) in scientific machine learning. By combining their strengths, it offers a robust method for solving parametric PDEs, which is crucial for simulations and digital twins in various engineering and scientific domains.