Optimal Uncertainty Quantification of SciML Models
Adam R. Gerlach, Avinash Subramanian, Benjamin Chung, Alexander Von Moll
We present OptimalUncertaintyQuantification.jl: A SciML package for end-to-end distributionally robust uncertainty quantification of static and dynamic systems models. The tool performs a worst-case analysis so as to make certification/decertification decisions on engineering models defined in ModelingToolkit.jl as demonstrated on a variety of aerospace and structural engineering applications.
Symbolic-Numeric Computing and Compiler-Enhanced Algorithms
Main Room 2