Physic Informed Machine

Physics-informed machine learning (PIML) integrates physical laws and data-driven models to improve the accuracy, efficiency, and interpretability of machine learning predictions. Current research focuses on developing novel architectures, such as physics-informed neural networks and kernel methods, to effectively incorporate physical constraints into the learning process, often addressing challenges like noisy data and high computational costs. This approach is proving valuable across diverse fields, enhancing model robustness in applications ranging from structural health monitoring and reservoir management to time series forecasting and material science, where incorporating physical principles leads to more reliable and efficient solutions. The resulting models often require less training data and offer improved generalization capabilities compared to purely data-driven methods.