Physics-Informed Machine Learning using Python – Training Services

Physics-Informed Machine Learning using Python – Training Services provides advanced training on integrating physical laws and domain knowledge into machine learning models for enhanced predictive capabilities. The course covers neural networks, PDE-constrained learning, surrogate modeling, and hybrid approaches that combine data-driven methods with governing physical equations. Participants will use Python libraries such as TensorFlow, PyTorch, and SciML to implement physics-informed models for mechanics, fluid dynamics, thermal systems, and quantum simulations. Emphasis is placed on model training, validation, generalization, and interpretability while ensuring consistency with physical principles. Case studies demonstrate applications in computational physics, materials modeling, and energy systems. Hands-on sessions provide practical experience in coding, training, and evaluating physics-informed machine learning models. By the end, participants will be proficient in leveraging machine learning while respecting fundamental physical constraints.