SciML Model

Scientific Machine Learning (SciML) integrates machine learning, particularly deep learning, with established scientific principles to model and predict complex phenomena, reducing reliance on extensive observational data. Current research emphasizes architectures like Physics-Informed Neural Networks (PINNs) and Neural Operators (NOs) for solving differential equations and learning complex relationships, as well as the development of robust, uncertainty-aware models using techniques like ensembling. This interdisciplinary field is significantly advancing scientific discovery across diverse domains, from seismology and materials science to hypersonic flow prediction, by enabling more efficient and accurate modeling of complex systems with limited data.