47 citations · 75 across the 13 of their papers we have counts for
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Hyper-differential sensitivity analysis in the context of Bayesian inference applied to ice-sheet problems
William Reese, Joseph Hart, Bart van Bloemen Waanders +3
Inverse problems constrained by partial differential equations (PDEs) play a critical role in model development and calibration. In many applications, there are multiple uncertain…
Hyper-differential sensitivity analysis with respect to model discrepancy: Optimal solution updating
Joseph Hart, Bart van Bloemen Waanders
A common goal throughout science and engineering is to solve optimization problems constrained by computational models. However, in many cases a high-fidelity numerical emulation o…
Estimating and using information in inverse problems
Wolfgang Bangerth, Chris R. Johnson, Dennis K. Njeru +1
In inverse problems, one attempts to infer spatially variable functions from indirect measurements of a system. To practitioners of inverse problems, the concept of "information" i…
Sensitivity driven experimental design to facilitate control of dynamical systems
Joseph Hart, Bart van Bloemen Waanders, Lisa Hood +1
Control of nonlinear dynamical systems is a complex and multifaceted process. Essential elements of many engineering systems include high fidelity physics-based modeling, offline t…
Hyper-differential sensitivity analysis for nonlinear Bayesian inverse problems
Isaac Sunseri, Alen Alexanderian, Joseph Hart +1
We consider hyper-differential sensitivity analysis (HDSA) of nonlinear Bayesian inverse problems governed by PDEs with infinite-dimensional parameters. In previous works, HDSA has…