Non-intrusive Learning of Physics-Informed Spatio-temporal Surrogate for Accelerating Design

ArXi:2604.14424v1 Announce Type: new Most practical engineering design problems involve nonlinear spatio-temporal dynamical systems. Multi-physics simulations are often performed to capture the fine spatio-temporal scales which govern the evolution of these systems. However, these simulations are often high-fidelity in nature, and can be computationally very expensive. Hence, generating data from these expensive simulations becomes a bottleneck in an end-to-end engineering design process.