Scientific Machine Learning and Artificial Intelligence: Uncertainty Quantification

The summary for the Scientific Machine Learning and Artificial Intelligence: Uncertainty Quantification grant is detailed below. This summary states who is eligible for the grant, how much grant money will be awarded, current and past deadlines, Catalog of Federal Domestic Assistance (CFDA) numbers, and a sampling of similar government grants. Verify the accuracy of the data FederalGrants.com provides by visiting the webpage noted in the Link to Full Announcement section or by contacting the appropriate person listed as the Grant Announcement Contact. If any section is incomplete, please visit the website for the Office of Science, which is the U.S. government agency offering this grant.
Scientific Machine Learning and Artificial Intelligence: Uncertainty Quantification: In support of the Executive Order on Maintaining American Leadership in Artificial Intelligence, the DOE Artificial Intelligence (AI) Program and DOE SC program in Advanced Scientific Computing Research (ASCR) hereby announce their interest in the co-design of learning systems and AI environments that significantly advance the field of AI for public benefit within DOE's Congressionally-authorized mission-space. The principal focus of this FOA is on Uncertainty Quantification (UQ) for AI validation and prediction. Foundational research is needed for strengthening the mathematical and statistical basis of validating machine learning and AI predictions from data generated by the Office of Science's user facilities and scientific simulations. A critical open question for scientific machine learning (SciML) is: How do we make reliable predictions and uncertainty estimates from machine learning and AI models? Predictions can be greatly improved by including input uncertainties and insights from model discrepancies. Research advances will be needed in methods that incorporate mathematical, statistical, scientific, and engineering principles for uncertainty estimates in extrapolative predictions. Furthermore, extensive literature in statistics can be leveraged for improving the model validation process. Advances in UQ will greatly enhance the mathematical and scientific computing foundations for accelerated research insights from SciML and AI.
Federal Grant Title: Scientific Machine Learning and Artificial Intelligence: Uncertainty Quantification
Federal Agency Name: Office of Science (PAMS-SC)
Grant Categories: Science and Technology
Type of Opportunity: Discretionary
Funding Opportunity Number: DE-FOA-0002122
Type of Funding: Grant
CFDA Numbers: 81.049
CFDA Descriptions: Information not provided
Current Application Deadline: May 31st, 2019
Original Application Deadline: May 31st, 2019
Posted Date: April 16th, 2019
Creation Date: April 16th, 2019
Archive Date: June 30th, 2019
Total Program Funding: $2,000,000
Maximum Federal Grant Award: $150,000
Minimum Federal Grant Award: $150,000
Expected Number of Awards: 6
Cost Sharing or Matching: No
Last Updated: April 16th, 2019
Applicants Eligible for this Grant
Unrestricted (i.e., open to any type of entity below), subject to any clarification in text field entitled "Additional Information on Eligibility"
Additional Information on Eligibility
All types of applicants are eligible to apply, except Federally Funded Research and Development Center (FFRDC) Contractors, and nonprofit organizations described in section 501(c)(4) of the Internal Revenue Code of 1986 that engaged in lobbying activities after December 31, 1995.Other Federal agencies are not eligible to receive financial assistance awards and are therefore ineligible both to submit applications and to be proposed as subawardees. Other Federal agency's FFRDCs are not eligible to submit applications or to be proposed as subawardees.
Link to Full Grant Announcement
Office of Science, Advanced Scientific Computing Research Funding Opportunity Page
Grant Announcement Contact
Steve Lee
Program Manager
Phone 301-903-5710
Program Manager Email
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