Physic Informed Loss

Physics-informed loss (PIL) integrates physical laws directly into the loss function of neural networks, improving the accuracy and efficiency of solving partial differential equations (PDEs) and related problems, particularly when data is scarce. Current research focuses on enhancing various neural network architectures, including recurrent neural networks (RNNs), physics-informed neural networks (PINNs), and variational autoencoders (VAEs), to effectively incorporate PIL and address challenges like numerical derivative computation and loss term imbalance. This approach significantly impacts scientific computing by enabling more accurate and efficient solutions to complex PDEs across diverse fields, from fluid dynamics and material science to satellite system fault detection.