We present an extensible software framework, hIPPYlib, for solution of large-scale
deterministic and Bayesian inverse problems governed by partial differential equations …

We present a mathematical framework and computational methods for optimally designing a
finite sequence of experiments. This sequential optimal experimental design (sOED) …

In calculating expected information gain in optimal Bayesian experimental design, the
computation of the inner loop in the classical double-loop Monte Carlo requires a large …

We apply the tensor train (TT) decomposition to construct the tensor product polynomial
chaos expansion (PCE) of a random field, to solve the stochastic elliptic diffusion PDE with …

We develop a framework for goal-oriented optimal design of experiments (GOODE) for large-
scale Bayesian linear inverse problems governed by PDEs. This framework differs from …

We provide an overview of the methods that can be used for prediction under uncertainty
and data fitting of dynamical systems, and of the fundamental challenges that arise in this …

Calibration experiment design optimization (CEDO) seeks to identify the optimal values of
experimental inputs in order to maximize the obtained information within testing budget …

Identifying modal coordinates from output-only data is a key link of virtual sensing
technology based on the modal extension method. It is also one of the goals of optimal …

We develop a fast method for optimally designing experiments in the context of statistical
seismic source inversion. In particular, we efficiently compute the optimal number and …

An optimal experimental set‐up maximizes the value of data for statistical inferences. The
efficiency of strategies for finding optimal experimental set‐ups is particularly important for …