Research Areas
Research Areas
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The FASTMath SciDAC Institute is developing and deploying scalable mathematical algorithms and software tools for reliable simulation of complex physical phenomena and collaborating with Department of Energy (DOE) domain scientists to ensure the usefulness and applicability of our work. The focus of our work is strongly driven by the requirements of DOE application scientists who require fast, accurate, and robust simulations along with the ability to efficiently perform ensembles of simulations in optimization or uncertainty quantification studies. Our crosscutting activities in computational efficiency and performance and in artificial intelligence ensure that our methods and software deliver the capabilities necessary to accelerate the domain science.
The Adaptive and Multiscale Discretization team develops and deploys technologies capable of providing accurate solutions to multiscale problems using architecture-aware methods that can leverage AI techinques and that can effectively execute on systems from CPU-only laptops to GPU-accelerated supercomputers.
The Algebraic Solvers research area encompasses fundamental research for scalable linear solvers, eigen solvers, and tensor solvers, targeting multiscale, coupled systems of equations. These solvers are one of the most common computational kernels in scientific applications of interest to the DOE. Efficient, scalable, and reliable algorithms are crucial for the success of large-scale simulations and data generation for AI methods.
The Decision Support Methods research area encompasses fundamental research in uncertainty quantification, numerical optimization, and the mathematics of digital twins, focusing on enhancing the reliability and effectiveness of computational sciences. These methods are pivotal for quantifying confidence in simulations, refining AI training and inference processes, and addressing complex parameter and state estimation challenges inherent in scientific research across the DOE.
The Computational Efficiency and Performance crosscut ensures that our algorithms and software enable application codes to fully utilize the exascale high-performance computing resources that the DOE continues to invert in, while minimizing energy waste from unnecessary data movement and computation.
The Artificial Intelligence crosscut is developing novel AI methods for scientific computing, using AI methods to improve our software, and delivering AI capabilities to applications across the DOE to accelerate their science.
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