Multi-fidelity physics constrained neural networks for dynamical systems

Physics-constrained neural networks are commonly employed to enhance prediction robustness compared to purely data-driven models, achieved through the inclusion of physical constraint losses during the model training process. However, one of the major challenges of physics-constrained neural networks consists of the training complexity especially for high-dimensional systems. In fact, conventional physics-constrained models rely on singular-fidelity data necessitating the assessment of physical constraints within high-dimensional fields, which introduces computational difficulties. Furthermore, due to the fixed input size of the neural networks, …

challenges complexity cs.lg data data-driven fidelity inclusion losses major networks neural networks physics physics.flu-dyn prediction process robustness systems through training