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 most pressing problems in these application areas have attributes that make them very different in nature to computer science applications where data-driven machine learning has found success.

First, these problems are typically characterized by complex multiscale multiphysics dynamics: Small changes in system parameters can lead to drastically large changes in system response. Approaches that simply try to interpolate — or extrapolate — the data are doomed to failure, no matter how expressive the underlying representation or large the training data set. Second, these problems have high-dimensional uncertain parameters that cannot be observed directly. When discretized, these are represented by parameter spaces of extremely high dimension — millions or even billions of degrees of freedom. Third, scientific and engineering data are often scarce: Experiments are costly, time-consuming, intrusive, and sometimes dangerous. Often data are the most difficult to acquire and are thus sparsest in the most decision-critical regions (e.g., when a system is close to failure or close to instability). Fourth, it is often rare events (e.g., failure) that drive the most critical decisions. It is not unusual to require design of critical engineering systems to be certified against probabilities of failure in the range of 10-6-10-9. Our models must provide reliable and robust predictions to support these decisions. Finally, for some applications, such critical decisions must be made on the fly in real time, leaving no time for re-training models on newly observed critical data.

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. Our 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.

To read more on the community's perspectives on Scientific Machine Learning, see the 2019 Department of Energy Report on Basic Research Needs for Scientific Machine Learning: Core Technologies for Artificial Intelligence.






Learning Optimal Aerodynamic Designs
ARPA-E Differentiate Program (O. Ghattas, K. Willcox)

AEOLUS: Advances in Experimental Design, Optimal Control, and Learning for Uncertain Complex Systems
A Department of Energy Mathematical Multifaceted Integrated Capability Center
(O. Ghattas, K. Willcox, G. Biros, R. Moser, J.T. Oden)

Multi-Fidelity Modeling of Rocket Combustor Dynamics
Air Force Center of Excellence (K. Willcox)

Quantum Machine Learning (C. Bajaj)

Rapid High-Fidelity Aerothermal Responses with Quantified Uncertainties via Reduced-Order Modeling
Sandia National Laboratories Autonomy for Hypersonics Initiative (K. Willcox)

Robust Deep Learning from Big Static & Dynamic Data (C. Bajaj)

SINC: System Integrated Neuromorphic Computing (C. Bajaj)