High-fidelity Multiphysics Modelling for Rapid Predictions Using Physics-informed Parallel Neural Operator
This provides a scalable and unsupervised framework for computational science and engineering, broadening the applicability of neural operators in multiphysics modelling.
The paper tackled the challenge of modelling complex multiphysics systems governed by nonlinear and strongly coupled PDEs, which are computationally expensive for traditional solvers and data-scarce for neural operators, by proposing a novel physics-informed parallel neural operator (PIPNO) that enables data-free, high-fidelity, and rapid predictions, outperforming existing operator learning approaches.
Modelling complex multiphysics systems governed by nonlinear and strongly coupled partial differential equations (PDEs) is a cornerstone in computational science and engineering. However, it remains a formidable challenge for traditional numerical solvers due to high computational cost, making them impractical for large-scale applications. Neural operators' reliance on data-driven training limits their applicability in real-world scenarios, as data is often scarce or expensive to obtain. Here, we propose a novel paradigm, physics-informed parallel neural operator (PIPNO), a scalable and unsupervised learning framework that enables data-free PDE modelling by leveraging only governing physical laws. The parallel kernel integration design, incorporating ensemble learning, significantly enhances both compatibility and computational efficiency, enabling scalable operator learning for nonlinear and strongly coupled PDEs. PIPNO efficiently captures nonlinear operator mappings across diverse physics, including geotechnical engineering, material science, electromagnetism, quantum mechanics, and fluid dynamics. The proposed method achieves high-fidelity and rapid predictions, outperforming existing operator learning approaches in modelling nonlinear and strongly coupled multiphysics systems. Therefore, PIPNO offers a powerful alternative to conventional solvers, broadening the applicability of neural operators for multiphysics modelling while ensuring efficiency, robustness, and scalability.