Physic Based Regularization

Physics-based regularization enhances machine learning models by incorporating physical laws and principles into the learning process, improving accuracy and generalization, particularly with limited data. Current research focuses on integrating this regularization into various architectures, including physics-informed neural networks (PINNs) and other deep learning models, to solve inverse problems and improve predictions in diverse fields like fluid dynamics, medical imaging, and robotics. This approach addresses challenges like uncertainty quantification and model misspecification, leading to more robust and reliable models for scientific simulations and real-world applications. The resulting improvements in model accuracy and efficiency have significant implications across scientific disciplines and engineering applications.