FASTMath AI/ML Capabilities

Researchers from across the FASTMath fields are working on all aspects of machine learning for DOE applications. At a high level, the research performed falls into the five categories, which are listed below. Several links to videos that outline the machine learning work done in FASTMath are provided.

  1. Robust accelerated learning. Data analytics, Solvers, Optimization, UQ, and Time integration methods in FASTMath are being employed to improve and scale learning methods.

  2. Predicting with confidence and complex predictions. Advances in optimization and UQ are providing machine learning the tools to work with uncertainty and make complex predictions requiring optimization of multiple loss functions simultaneously.

  3. Augmented and reinforced learning. Data analytics and UQ are being employed to augment data when measurements are limited and suggest new measurements that will maximize understanding of predictive systems.

  4. Embedding physical understanding into predictions. FASTMath has extensive experience with solving ODE/PDEs. Researchers have been using experimental design and reduced order modeling techniques to infuse physical understanding into machine learning methods.

  5. Data assimilation and model calibration. Our understanding of PDEs and machine learning is being combined to accelerate assimilation of complex data and calibration of models in high dimensional parameter space.

FASTMath researchers are using the tools and theory designed in their fields of research to bring unique understanding and fundamental developments to the five categories mentioned above. FASTMath is designing software that allows these mathematical developments to be used at scale on leadership computing facilities.