Good Afternoon,
The Department of Energy has released a funding opportunity titled “Scientific Machine Learning for Complex Systems,” which limits the number of submissions from an institution. To ensure that the institutional limit is not exceeded, interested applicants are asked to submit a brief expression of interest by February 15, 2023. More information can be found in the synopses below:
Harvard Expression of Interest Deadline: February 15, 2023
FAS/SEAS/OSP Pre-Application Deadline: February 24, 2023
Sponsor Pre-Application Deadline: March 1, 2023 by 5:00PM
FAS/SEAS/OSP Full Proposal Deadline: April 5, 2023
Sponsor Full Proposal Deadline: April 12, 2023
Award Amount: Up to $1,200,000 per year for four years
The DOE Office of Science program in Advanced Scientific Computing Research has announced 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. 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. PRD #5 (Machine Learning-Enhanced Modeling and Simulation) and uncertainty quantification are the subject of this FOA.
The focus of this funding opportunity is on basic research and development at the intersection of uncertainty quantification (UQ) and scientific machine learning (SciML) applied to the modeling and simulation of complex systems and processes. Scientific computing within the DOE traditionally has been dominated by complex, resource-intensive numerical simulations. However, the rise of data-driven SciML models and algorithms provides new opportunities. Traditional scientific computing forward simulations often are referred to as “inner loop” modeling. The combination of traditional scientific computing expertise and machine learning-based adaptivity and acceleration has the potential to increase the performance and throughput of inner-loop modeling. Such hybrid modeling and simulation approaches offer the opportunity, for example, to combine the versatility of neural networks for function and operator approximations, the domain-knowledge and interpretability of differential equations and operators, and the robustness of high-performance scientific computing software across these areas.
Applications submitted in response to this FOA must substantially address the research topic area and the following three facets in advanced scientific computing:
- Impact: What are the most significant or compelling scientific or technical challenges that are driving the development of the proposed approaches?
- Methodology: In what ways will the proposed research provide new and/or significant enabling technology for scientific computing? What are the potential merits and limitations, particularly with respect to current and emerging high-end computing architectures and ecosystems?
- Validation: What is a relevant set of non-trivial metrics for assessing the accuracy and effectiveness of the proposed approaches?
Applicant institutions are limited to no more than four pre-applications or applications as the lead institution in a single- or multi-institution team and no more than two pre-applications for each PI. Interested applicants are asked to email a brief expression of interest to Erin Hale at erin_hale@fas.harvard.edu no later than February 15, 2023. Expressions in interest should include the names and affiliations of the PI(s), title of proposal, and a brief description of the proposed project. More information may be requested if there are more than four interested applicants.