IP-basis PINNs: efficient multi-query inverse parameter estimation

Abstract Solving inverse problems with physics-informed neural networks (PINNs) is computationally expensive for multi-query scenarios, as each new set of observed data requires a new, expensive training procedure. We present inverse-parameter basis PINNs (IP-Basis PINNs), a meta-learning framework that extends previous work to enable rapid and efficient inference for multi-query inverse problems. Our method employs an offline–online decomposition: a deep network is first trained offline to produce a rich set of basis functions that span the solution space of a parametric differential equation. Online, for each new inverse problem, this network is frozen, then solutions and parameters are inferred by training only a lightweight linear output layer against observed data. Key innovations that make our approach effective for inverse problems include: (1) a novel online loss formulation for simultaneous solution reconstruction and parameter identification, (2) a significant reduction in computational overhead via forward-mode automatic differentiation for PDE loss evaluation, and (3) a non-trivial validation and early-stopping mechanism for robust offline training. We demonstrate the efficacy of IP-Basis PINNs on three diverse benchmarks, including an extension to universal PINNs for unknown functional terms—showing consistent performance across constant and functional parameter estimation, a significant speedup per query over standard PINNs, and robust operation with scarce and noisy data.