2 citations · 8 across the 9 of their papers we have counts for
9 papers
Surrogate-based Bayesian calibration methods for chaotic systems: a comparison of traditional and non-traditional approaches
Maike F. Holthuijzen, Atlanta Chakraborty, Elizabeth Krath +1
Parameter calibration is essential for reducing uncertainty and improving predictive fidelity in physics-based models, yet it is often limited by the high computational cost of mod…
Curvature in the Looking-Glass: Optimal Methods to Exploit Curvature of Expectation in the Loss Landscape
Jed A. Duersch, Tommie A. Catanach, Alexander Safonov +1
Harnessing the local topography of the loss landscape is a central challenge in advanced optimization tasks. By accounting for the effect of potential parameter changes, we can alt…
Analysis and Optimization of Seismic Monitoring Networks with Bayesian Optimal Experiment Design
Jake Callahan, Kevin Monogue, Ruben Villarreal +1
Monitoring networks increasingly aim to assimilate data from a large number of diverse sensors covering many sensing modalities. Bayesian optimal experimental design (OED) seeks to…
A Likelihood-Free Approach to Goal-Oriented Bayesian Optimal Experimental Design
Atlanta Chakraborty, Xun Huan, Tommie Catanach
Conventional Bayesian optimal experimental design seeks to maximize the expected information gain (EIG) on model parameters. However, the end goal of the experiment often is not to…
Development of Bayesian Component Failure Models in E1 HEMP Grid Analysis
Niladri Das, Ross Guttromson, Tommie A. Catanach
Combined electric power system and High-Altitude Electromagnetic Pulse (HEMP) models are being developed to determine the effect of a HEMP on the US power grid. The work relies pri…
Goal-Oriented Bayesian Optimal Experimental Design for Nonlinear Models using Markov Chain Monte Carlo
Shijie Zhong, Wanggang Shen, Tommie Catanach +1
Optimal experimental design (OED) provides a systematic approach to quantify and maximize the value of experimental data. Under a Bayesian approach, conventional OED maximizes the…
Metrics for Bayesian Optimal Experiment Design under Model Misspecification
Tommie A. Catanach, Niladri Das
The conventional approach to Bayesian decision-theoretic experiment design involves searching over possible experiments to select a design that maximizes the expected value of a sp…
Learning a quantum computer's capability
Daniel Hothem, Kevin Young, Tommie Catanach +1
Accurately predicting a quantum computer's capability -- which circuits it can run and how well it can run them -- is a foundational goal of quantum characterization and benchmarki…
Design of experiments for the calibration of history-dependent models via deep reinforcement learning and an enhanced Kalman filter
Ruben Villarreal, Nikolaos N. Vlassis, Nhon N. Phan +5
Experimental data is costly to obtain, which makes it difficult to calibrate complex models. For many models an experimental design that produces the best calibration given a limit…