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X-WR-CALNAME;VALUE=TEXT:DOE Scientific Machine Learning and Artificial Intelligence: Uncertainty Quantification: Sponsor Letter of Intent Deadline
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SUMMARY:DOE Scientific Machine Learning and Artificial Intelligence: Uncertainty Quantification: Sponsor Letter of Intent Deadline
DESCRIPTION:<strong><a href="https://www.grants.gov/web/grants/view-opportunity.html?oppId=314981" target="_blank" title="">DOE Scientific Machine Learning and Artificial Intelligence: Uncertainty Quantification</a><br>Sponsor Deadline for Letters of Intent (required): May 8, 2019<br>FAS/SEAS/OSP Deadline: May 23, 2019<br>Sponsor Deadline for Full Proposals: May 31, 2019<br>Award Information: $150,000 per year for 2 years. Approximately 2-6 awards are expected. </strong><br> <br>In support of the Executive Order on Maintaining American Leadership in Artificial Intelligence, the DOE Artificial Intelligence (AI) Program and DOE Office of Science (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.<br> <br>DOE will not consider funding multi-institution collaborations under this FOA. An individual may participate in no more than two applications. If the same individual is a project member on more than two applications, the most recently received applications that match a qualified Letter of Interest (LOI) will be accepted and all other applications may be declined without merit review.
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STATUS:CONFIRMED
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DTEND:20190508T210000Z
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