Institutes

There are currently 3 SciDAC institutes with over 24 participating institutions. The mission of the SciDAC-5 institutes is to provide intellectual resources in applied mathematics and computer science, expertise in algorithms and methods, and scientific software tools to advance scientific discovery through modeling and simulation in areas of strategic importance to the US Department of Energy (DOE) and the DOE Office of Science (SC).

Specific goals and objectives for the SciDAC institutes are to support, complement, or develop the following:

• Tools and resources for lowering the barriers to effectively use state-of-the-art computational systems.
• Mechanisms for taking on computational grand challenges across different science application areas.
• Mechanisms for incorporating and demonstrating the value of basic research results from applied mathematics and computer science.
• Plans for building up and engaging our nation's computational science research communities.

Current SciDAC Institutes

FASTMath — Frameworks, Algorithms, and Scalable Technologies for Mathematics

The FASTMath Institute develops and deploys scalable mathematical algorithms and software tools for reliable simulation of complex physical phenomena and collaborates with domain scientists to ensure the usefulness and applicability of FASTMath technologies.

Institute Director: Carol Woodward, Lawrence Livermore National Laboratory

DOE Program Manager: Xujing Davis

RAPIDS—SciDAC Institute for Computer Science and Data

The RAPIDS Institute solves computer science and data technical challenges for SciDAC and SC science teams, works directly with SC scientists and DOE facilities to adopt and support RAPIDS technologies, and coordinates with other DOE computer science and applied mathematics activities to maximize impact on SC science.

Institute Director: Rob Ross, Argonne National Laboratory

DOE Program Manager: Marco Fornari

LEADS—SciDAC Institute for LEarning-Accelerated Domain Science

LEADS introduces a paradigm shift by integrating SciML directly into domain-specific challenges. Working closely with domain scientists, the LEADS team will structure their SciML approach to domain science problems in various complexity levels and assign the proper SciML capability. LEADS is uniquely positioned to address phenomena where traditional numerical methods may be insufficient and expand DOE’s capability in using machine learning for domain science.

Institute Director: Panos Stinis, Pacific Northwest National Laboratory

DOE Program Manager: Xujing Davis

Last updated: 15 May 2026