Physical Constraint

Physical constraint in scientific modeling focuses on integrating known physical laws and principles into machine learning models to improve accuracy, efficiency, and interpretability. Current research emphasizes incorporating these constraints within various architectures, including neural networks (e.g., Physics-Informed Neural Networks, Convolutional Neural Networks, Transformers), Gaussian processes, and normalizing flows, often employing techniques like differentiable optimization and constrained optimization to enforce these constraints. This approach is proving valuable across diverse fields, from cosmology and materials science to fluid dynamics and robotics, by enhancing model robustness, reducing data requirements, and enabling more reliable predictions of complex systems.