Scientific Machine Learning

Scientific machine learning (SciML) is an emerging field that lies at the intersection of physical modeling and simulation (based mostly on numerical partial differential equations) and data-driven machine learning (based mostly on neural networks and deep learning). By integrating these two different approaches (physics-based and data-based), SciML produces novel computational methods that inherit the attractive features of both approaches. In particular, SciML algorithms should have the following desirable properties: convergence estimates, robustness or generalizability, and computational efficiency.

The Ken Kennedy Institute partially supports the Scientific Machine Learning Cluster at Rice University.