Search Results for author:
Found 5 papers, 1 papers with code
Robust A-Optimal Experimental Design for Bayesian Inverse Problems
This work presents an efficient algorithmic approach for designing optimal experimental design schemes for Bayesian inverse problems such that the optimal design is robust to misspecification of elements of the inverse problem.
Modeling Design and Control Problems Involving Neural Network Surrogates
We consider nonlinear optimization problems that involve surrogate models represented by neural networks.
Learning Symbolic Expressions: Mixed-Integer Formulations, Cuts, and Heuristics
This problem is referred to as symbolic regression.
Stochastic Learning Approach to Binary Optimization for Optimal Design of Experiments
We present a novel stochastic approach to binary optimization for optimal experimental design (OED) for Bayesian inverse problems governed by mathematical models such as partial differential equations.
Joint ptycho-tomography with deep generative priors
In our simulations, we demonstrate that our proposed framework with parameter tuning and learned priors generates high-quality reconstructions under limited and noisy measurement data.
