January 24, 2023 -
Funding Opportunity Announcement (FOA) Number: DE-FOA-0002958
Total Estimated Funding: $16 million
Funding for basic research to explore potentially high-impact approaches in the development and use of scientific machine learning and artificial intelligence in the predictive modeling, simulation, and analysis of complex systems and processes.
The focus of this funding opportunity announcement 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 Department of Energy 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. Relevant domains of application include materials, environmental, and life sciences; high-energy, nuclear and plasma physics, and the DOE Energy Earthshots Initiative, for example. While it is anticipated that proposed projects will focus on specific complex systems, the applied mathematics research advances must have more general applicability.
Please see the funding opportunity for agency contacts and more details, including eligibility and application information.