PINN Training

Physics-Informed Neural Networks (PINNs) leverage neural networks to solve partial differential equations (PDEs), integrating physical laws into the model training process to improve accuracy and generalization, particularly with limited data. Current research emphasizes optimizing PINN training by strategically selecting training points and employing techniques like multi-task optimization and biobjective optimization to balance competing loss functions (e.g., data fit versus adherence to the PDE). These advancements aim to enhance PINN efficiency and scalability, enabling applications in diverse fields requiring real-time solutions to complex PDEs, such as traffic flow prediction and fluid dynamics simulations.