4 papers
Hyper-differential sensitivity analysis with respect to model discrepancy: Sequential optimal experimental design
Madhusudan Madhavan, Joseph Hart, Bart van Bloemen Waanders
Large-scale optimization problems are ubiquitous in the physical sciences; yet, high-fidelity models can often be complex and computationally prohibitive for optimization. A practi…
Preconditioned pseudo-time continuation for parameterized inverse problems
Joseph Hart, Alen Alexanderian, Bart van Bloemen Waanders
We consider parameterized variational inverse problems that are constrained by partial differential equations (PDEs). We seek to efficiently compute the solution of the inverse pro…
Hyper-differential sensitivity analysis with respect to model discrepancy: Prior distributions
Joseph Hart, Bart van Bloemen Waanders, Jixian Li +2
Hyper-differential sensitivity analysis with respect to model discrepancy was recently developed to enable uncertainty quantification for optimization problems. The approach consis…
Toward real-time optimization through model reduction and model discrepancy sensitivities
Joseph Hart, Shane A. McQuarrie, Zachary Morrow +1
Optimization problems arise in a range of scenarios, from optimal control to model parameter estimation. In many applications, such as the development of digital twins, it is essen…