Scientific Machine Learning for Complex Systems

https://www.grants.gov/web/grants/view-opportunity.html?oppId=345532
Award Amount
$1,200,000.00
Maximum Amount
$1,200,000.00
Assistance Type
Grant
Funding Source
Federal
Implementing Entity
Office of Science
Due Date
Where the Opportunity is Offered
All of California
Eligible Applicant
Other
Additional Eligibility Information
All types of applicants are eligible to apply, except nonprofit organizations described in section 501(c)(4) of the Internal Revenue Code of 1986 that engaged in lobbying activities after December 31, 1995. Federally affiliated entities must adhere to the eligibility standards below: 1. DOE/NNSA National Laboratories DOE/NNSA National Laboratories are eligible to submit applications (either as a lead organization or as a team member in a multi-institutional team) under this FOA and may be proposed as subrecipients under another organization’s application. If recommended for funding as a lead applicant, funding will be provided through the DOE Field-Work Proposal System and work will be conducted under the laboratory’s contract with DOE. No administrative provisions of this FOA will apply to the laboratory or any laboratory subcontractor. If recommended for funding as a proposed subrecipient, the value of the proposed subaward will be removed from the prime applicant’s award and will be provided to the laboratory through the DOE Field-Work Proposal System and work will be conducted under the laboratory’s contract with DOE. Additional instructions for securing authorization from the cognizant Contracting Officer are found in Section VIII of this FOA. 2. Non-DOE/NNSA FFRDCs Non-DOE/NNSA FFRDCs are eligible to submit applications (either as a lead organization or as a team member in a multi-institutional team) under this FOA and may be proposed as subrecipients under another organization’s application. If recommended for funding as a lead applicant, funding will be provided through an interagency agreement Award to the FFRDC’s sponsoring Federal Agency. If recommended for funding as a proposed subrecipient, the value of the proposed subaward may be removed from the prime applicant’s award and may be provided through an Inter-Agency Award to the FFRDC’s sponsoring Federal Agency. Additional instructions for securing authorization from the cognizant Contracting Officer are found in Section VIII of this FOA. 3. Other Federal Agencies Other Federal Agencies are neither eligible to submit applications under this FOA nor to be proposed as subrecipients under another organization’s application.
Contact
Steven.Lee@science.doe.gov
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.

Category
Other

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