Physical Model

Physical modeling aims to represent real-world processes using mathematical equations, often coupled with data-driven approaches to improve accuracy and address model limitations. Current research emphasizes integrating physical models with machine learning techniques, such as neural networks (including Physics-Informed Neural Networks and generative adversarial networks), Gaussian processes, and symbolic regression, to enhance model accuracy, interpretability, and generalizability. This interdisciplinary approach is significantly impacting various fields, from improving medical treatment planning and simulating complex systems like traffic flow to advancing scientific understanding in areas like geoscience and material science through more accurate and efficient modeling.