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20222025
most citedDesign of experiments for the calibration of history-dependent models via deep reinforcement learning and an enhanced Kalman filter

2 citations · 8 across the 9 of their papers we have counts for

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stat.ME2025

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…

stat.ME2023★ 1 cited

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…