College of Engineering, Design & Computing Events

Pre-proposal due 1 March | Scientific Machine Learning for Complex Systems

| All Day
Contact :
Alison Pearks
Email :


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. 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.