Closed · DE-FOA-0002958 · CFDA 81.049 · Discretionary
Scientific Machine Learning for Complex Systems
Federal grant opportunity posted by Office of Science, cataloged on Grants.gov.
- $300K-$1.2M
- Award range
- Closed
- Status
- April 19, 2023
- Close date
- -
- Expected awards
The verdict
Scientific Machine Learning for Complex Systems is a closed discretionary listing from Office of Science that offered $300,000 -- $1,200,000. Future funding cycles may be published under the same CFDA number.
- $300K-$1.2M
- award range
- Closed
- application status
- 81.049
- CFDA program
Opportunity snapshot. This Grants.gov announcement - Scientific Machine Learning for Complex Systems - is cataloged under number DE-FOA-0002958 and tied to CFDA assistance listing 81.049, posted by Office of Science. Grants.gov currently shows the opportunity as closed, first posted on January 24, 2023 and last updated on March 20, 2023. The funding category is Discretionary, delivered as a grant.
Award economics. The award range on file is $300,000 -- $1,200,000. The agency has projected $16.0 million in total estimated funding for this announcement. Cost sharing is not required, so applicants do not need to commit matching funds to be competitive on this opportunity. Federal award ranges are often upper bounds; actual allocations reflect program appropriations, the strength of the applicant pool, and the evaluation committee's scoring.
Deadline and action path. This opportunity closed on April 19, 2023. Future funding cycles may be published under the same CFDA number, so monitoring the parent program page is the most reliable way to catch re-announcements. Every Grants.gov submission requires an active SAM.gov registration and a Unique Entity ID. Review the Eligibility section below carefully, federal eligibility categories (nonprofit, state or local government, tribal, individual, educational institution, small business) have distinct registration and reporting requirements. Pre-application outreach to the listed agency contact is permitted and often welcomed, it helps clarify scope and scoring priorities. Before acting on the deadline or award figures above, verify them directly on the official Grants.gov listing, amendments can change dates and amounts after this page was last refreshed.
Award Range
$300,000 -- $1,200,000
Close Date
April 19, 2023
Posted
January 24, 2023
Est. Total Funding
$16,000,000
Instrument
Grant
Description
The DOE SC program in Advanced Scientific Computing Research (ASCR) hereby announces its interest in research applications to explore potentially high-impact approaches in the development and use of scientific machine learning (SciML) and artificial intelligence (AI) in the predictive modeling, simulation and analysis of complex systems and processes.High-performance computational models, simulations, algorithms, data from experiments and observations, and automation are being used to accelerate scientific discovery and innovation. Recent workshops, report, and strategic plans across the DOE have highlighted the research, development, and use of artificial intelligence and machine learning for science, energy, and security. Relevant domains include materials, environmental, and life sciences; high-energy, nuclear, and plasma physics; and the DOE Energy Earthshots Initiative, for examples. A 2018 Basic Research Needs workshop and report on scientific machine learning (SciML) and AI identified six Priority Research Directions (PRDs) for the development of the broad foundations and research capabilities needed to address such DOE mission priorities. The first three PRDs for foundational research are a set of themes common to all SciML approaches and correspond to the need for domain-awareness, interpretability, and robustness and scalability, respectively. Of the other three PRDs for capability research, PRD #5 (Machine Learning-Enhanced Modeling and Simulation) and uncertainty quantification are the subject of this FOA.DOE is committed to promoting the diversity of investigators and institutions it supports, as indicated by the ongoing use of program policy factors (see Section V) in making selections of awards. To strengthen this commitment, DOE encourages applications that are led by, or include partners from Established Program to Stimulate Competitive Research (EPSCoR) states, that are underrepresented in the ASCR portfolio and applications led by individuals from groups historically underrepresented in STEM.
Eligibility
Grants.gov lists this opportunity under eligibility category code 99. These codes correspond to applicant types (state/local government, tribal organization, nonprofit, educational institution, individual, small business, etc.) defined in Grants.gov's own eligibility reference. See the current Grants.gov eligibility categories or check the official listing below for this opportunity's exact eligibility statement.
Official Listing on Grants.gov
View full details, application forms, and submission instructions.
Parent Grant Program
Basic Energy Sciences
U.S. Department of Energy
Agency Contact
Steven.Lee@science.doe.gov
Key Dates
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| Sources | the SAM.gov Assistance Listings and Grants.gov |