Center for Scientific Machine Learning
Scientific Machine Learning is an emerging research area focused on the opportunities and challenges of machine learning in the context of complex applications across science, engineering, and medicine. The Center for Scientific Machine Learning is addressing these challenges through the development of new methods that weave together the perspectives of the field of Computational Science and Engineering — perspectives grounded in structured physics-based modeling where enforcing the governing physical laws brings the power to constrain an otherwise intractable solution space — and the perspectives of data-driven machine learning. The group's research projects bring together a diverse range of computing theories and algorithms, including large-scale optimization, inverse theory, reduced-order modeling, uncertainty quantification, Bayesian inference, optimal experimental design, data assimilation, physics-informed deep learning, interpretable machine learning, reinforcement learning, and high performance computing.
