Democratizing uncertainty quantification.

Further analysis of multilevel Stein variational gradient descent with an application to the Bayesian inference of glacier ice models.

Point Spread Function Approximation of High-Rank Hessians with Locally Supported Nonnegative Integral Kernels.

A Scalable Interior-Point Gauss-Newton Method for PDE-Constrained Optimization with Bound Constraints.

Democratizing Uncertainty Quantification.

hIPPYlib-MUQ: A Bayesian Inference Software Framework for Integration of Data with Complex Predictive Models under Uncertainty.

On the implementation of a quasi-Newton interior-point method for PDE-constrained optimization using finite element discretizations.

Point spread function approximation of high rank Hessians with locally supported non-negative integral kernels.

Hierarchical off-diagonal low-rank approximation of Hessians in inverse problems, with application to ice sheet model initializaiton.

Optimal design of large-scale nonlinear Bayesian inverse problems under model uncertainty.

hIPPYlib: An Extensible Software Framework for Large-Scale Inverse Problems Governed by PDEs: Part I: Deterministic Inversion and Linearized Bayesian Inference.

Optimal Design of Large-scale Bayesian Linear Inverse Problems Under Reducible Model Uncertainty: Good to Know What You Don't Know.

Statistical Treatment of Inverse Problems Constrained by Differential Equations-Based Models with Stochastic Terms.

hIPPYlib: An Extensible Software Framework for Large-Scale Inverse Problems.

Mean-Variance Risk-Averse Optimal Control of Systems Governed by PDEs with Random Parameter Fields Using Quadratic Approximations.

A Fast and Scalable Method for A-Optimal Design of Experiments for Infinite-dimensional Bayesian Nonlinear Inverse Problems.

Scalable and efficient algorithms for the propagation of uncertainty from data through inference to prediction for large-scale problems, with application to flow of the Antarctic ice sheet.

A Computational Framework for Infinite-Dimensional Bayesian Inverse Problems, Part II: Stochastic Newton MCMC with Application to Ice Sheet Flow Inverse Problems.

A-Optimal Design of Experiments for Infinite-Dimensional Bayesian Linear Inverse Problems with Regularized ℓ₀-Sparsification.

Modeling and Design Optimization of a Resonant Optothermoacoustic Trace Gas Sensor.