47 citations · 75 across the 13 of their papers we have counts for
14 papers
A Simulation-Grounded Agentic VLM Framework for Wildfire Monitoring and Reporting
Duowen Chen, Yuchen Sun, Zhiqi Li +4
Effective wildfire monitoring requires relating visual evidence to physical fire dynamics, yet real videos with synchronized physical annotations are scarce and high-fidelity 3D si…
Risk-Aware Goal-Oriented Bayesian Optimal Experimental Design
John D. Jakeman, Rebekah White, Bart van Bloemen Waanders +2
Traditional Bayesian optimal experimental design (OED) selects measurements that best inform a model's parameters. However, such measurements can be suboptimal for downstream predi…
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…
Neural Network Approaches for Parameterized Optimal Control
Deepanshu Verma, Nick Winovich, Lars Ruthotto +1
We consider numerical approaches for deterministic, finite-dimensional optimal control problems whose dynamics depend on unknown or uncertain parameters. We seek to amortize the so…
Hyper-differential sensitivity analysis with respect to model discrepancy: Posterior Optimal Solution Sampling
Joseph Hart, Bart van Bloemen Waanders
Optimization constrained by high-fidelity computational models has potential for transformative impact. However, such optimization is frequently unattainable in practice due to the…
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…
Enabling hyper-differential sensitivity analysis for ill-posed inverse problems
Joseph Hart, Bart van Bloemen Waanders
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 for Inverse Problems Constrained by Partial Differential Equations
Isaac Sunseri, Joseph Hart, Bart van Bloemen Waanders +1
High fidelity models used in many science and engineering applications couple multiple physical states and parameters. Inverse problems arise when a model parameter cannot be deter…
Fractional Operators Applied to Geophysical Electromagnetics
Chester J. Weiss, Bart G. van Bloemen Waanders, Harbir Antil
A growing body of applied mathematics literature in recent years has focussed on the application of fractional calculus to problems of anomalous transport. In these analyses, the a…
Decreasing the temporal complexity for nonlinear, implicit reduced-order models by forecasting
Kevin Carlberg, Jaideep Ray, Bart van Bloemen Waanders
Implicit numerical integration of nonlinear ODEs requires solving a system of nonlinear algebraic equations at each time step. Each of these systems is often solved by a Newton-lik…