An Efficient Multi‐Physics GPT‐PINN Framework for Predicting Reactive Solute Transport in Parameterized Groundwater Systems

Abstract Modeling coupled groundwater flow and reactive transport for multi‐query tasks is computationally prohibitive, and standard Physics‐Informed Neural Networks (PINNs) require costly retraining for each new parameter. We introduce the Multi‐Physics Generative Pre‐trained PINN (MP‐GPT‐PINN), a meta‐learning framework to resolve this bottleneck. Its innovation is a two‐stage strategy: an offline pre‐training stage uses a greedy algorithm to build a compact library of PINN solutions for key parameters. A rapid online stage then generates new solutions by combining these pre‐trained bases in an optimal linear manner. For multi‐physics problems, we designed a parallel dual‐network architecture to couple the seepage and chemical fields robustly. Validated across complex groundwater systems, MP‐GPT‐PINN accelerates online predictions by four orders of magnitude while maintaining high fidelity, with dimensionless relative errors for physical fields approaching 10 −3 . This efficiency breakthrough makes computationally intensive tasks, such as large‐scale parameter inversion and uncertainty quantification, feasible in geosciences.