arXiv Machine Learning August 27, 2026 By Ahmad Ishaque Karimi, Uvini Balasuriya Mudiyanselage, Kookjin Lee

Physics-Informed Foresight Pruning for Sparse PINN Solvers of Nonlinear PDEs

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Physics-informed neural networks (PINNs) often use over‑parameterized models, raising questions about necessary capacity and effective pruning. This study introduces physics‑informed spectrum‑aware pruning (PI‑SAP), which assigns saliency based on PDE residual sensitivity, and compares it to standard NTK‑SAP. Experiments on several nonlinear PDEs show PI‑SAP consistently preserves residual fidelity and remains competitive under aggressive sparsity, though no single criterion is optimal across all equations or sparsity levels.

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