Physic Informed Training

Physics-informed training (PIT) enhances machine learning models by incorporating physical principles and prior knowledge into the training process, improving accuracy and robustness, especially in data-scarce scenarios. Current research focuses on integrating PIT with various architectures, including neural networks (both standard and operator-based), generative adversarial networks (GANs), and large language models (LLMs), often employing techniques like transfer learning, multi-fidelity approaches, and multi-objective optimization algorithms to overcome challenges in training complex models. This approach is proving valuable across diverse fields, from materials science and engineering to medical imaging and quantum physics, by enabling more accurate and efficient modeling of complex systems where experimental data is limited or expensive to obtain.