A fast general thermal simulation model based on Multi-Branch Physics-Informed deep operator neural network

Thermal simulation plays a crucial role in various fields, often involving complex partial differential equation (PDE) simulations for thermal optimization. To tackle this challenge, we have harnessed neural networks for thermal prediction, specifically employing deep neural networks as a universal solver for PDEs. This innovative approach has garnered significant attention in the scientific community. While Physics-Informed Neural Networks (PINNs) have been introduced for thermal prediction using deep neural networks, existing methods primarily focus on offering thermal simulations for predefined relevant parameters, such as heat sources, loads, boundaries, and initial conditions. However, any adjustments to these parameters typically require retraining or transfer learning, resulting in considerable additional work. To overcome this limitation, we integrated PINN methods with the DeepONet model, creating a novel model called PI-DeepONet for thermal simulation. This model takes both relevant parameters and coordinate points as simultaneous input functions, presenting a fresh computational perspective for thermal simulation. Based on the PaddlePaddle deep learning framework, our research demonstrates that after sufficient training, this model can reliably and rapidly predict parameter solutions. Importantly, it significantly surpasses traditional numerical solvers in terms of speed by several orders of magnitude, without requiring additional training. This groundbreaking research framework holds vast application potential and promises substantial advancements in the field of thermal simulation.