Scalable AI-Driven Multiphysics Simulation Frameworks for Next-Generation Computational Engineering

Abstract

Computational Science and Engineering increasingly demands scalable solvers capable of resolving tightly coupled multiphysics systems with high hardware utilization and low predictive variance. This article proposes a scalable AI-driven multiphysics simulation framework that integrates physics-informed operator learning, adaptive resolution control, and hybrid solver orchestration to support next-generation computational engineering. The objective is to achieve early convergence of reusable surrogate operators while maximizing compute returns on distributed GPU environments without sacrificing physical consistency. The methodology combines classical numerical solvers for high-resolution data generation, physics-informed neural networks (PINNs) for fluid-thermal operators, graph neural networks (GNNs) for mesh-based electromagnetic learning, MPI-enabled multi-node execution, AI-guided adaptive mesh refinement, and hybrid correction loops for stability preservation. Results demonstrate that the AI surrogate solver delivers 5.9× speedup at 16 GPUs, outperforming classical parallel solvers by more than 2× at equal scale, while hybrid solving achieves 4.8×. Heat-PINN stabilizes at 0.03 loss by epoch 6000, and EM-GNN converges early at 0.002 loss by epoch 660. Validation confirms error reductions to 1.7% (thermal), 1.5% (structural), and 0.9% (EM), compressing classical solver error spread of 1–21% into 1–10%. The framework proves that scalability must jointly address learning and hardware utilization, establishing a reliable foundation for real-time digital-twin analysis and large-scale engineering simulations.