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arXiv · 2511.00792

Fast PINN Eigensolvers via Biconvex Reformulation

Akshay Sai Banderwaar·Abhishek Gupta
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Abstract

Eigenvalue problems have a distinctive forward-inverse structure and are fundamental to characterizing a system's thermal response, stability, and natural modes. Physics-Informed Neural Networks (PINNs) offer a mesh-free alternative for solving such problems but are often orders of magnitude slower than classical numerical schemes. In this paper, we introduce a reformulated PINN approach that casts the search for eigenpairs as a biconvex optimization problem, enabling fast and provably convergent alternating convex search (ACS) over eigenvalues and eigenfunctions using analytically optimal updates. Numerical experiments show that PINN-ACS attains high accuracy with convergence speeds up to 500$\times$ faster than gradient-based PINN training. We release our codes at https://github.com/NeurIPS-ML4PS-2025/PINN_ACS_CODES.

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Akshay Sai Banderwaar, Abhishek Gupta. 2025-11-02. Fast PINN Eigensolvers via Biconvex Reformulation. https://arxiv.org/abs/2511.00792

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