Four new acquisition functions are developed for Bayesian quadrature to measure and reduce prediction uncertainties in posterior and evidence estimation, extended to transitional schemes for robust performance on complex posteriors.
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A method infers a spatially resolved constitutive precision field in elastography by comparing equilibrium-required stress against model-predicted stress using separate precision hyperparameters.
A methodology for populational inverse problems that simultaneously deconvolves unknown observational noise and recovers parameter distributions via structured gradient descent and adaptive empirical measure-based active learning for surrogates.
Multi-agent LLM system applies set-based design and Conditional Value-at-Risk to explore and risk-filter airfoil designs with human manager coordination.
citing papers explorer
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Bayesian Active Learning for Bayesian Model Updating: the Art of Acquisition Functions and Beyond
Four new acquisition functions are developed for Bayesian quadrature to measure and reduce prediction uncertainties in posterior and evidence estimation, extended to transitional schemes for robust performance on complex posteriors.
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The Illusion of Fit: Spatially Resolved Assessment of Constitutive Model Validity in Elastography and Physics-Based Inverse Problems
A method infers a spatially resolved constitutive precision field in elastography by comparing equilibrium-required stress against model-predicted stress using separate precision hyperparameters.
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Efficient Deconvolution in Populational Inverse Problems
A methodology for populational inverse problems that simultaneously deconvolves unknown observational noise and recovers parameter distributions via structured gradient descent and adaptive empirical measure-based active learning for surrogates.
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Agentic Risk-Aware Set-Based Engineering Design
Multi-agent LLM system applies set-based design and Conditional Value-at-Risk to explore and risk-filter airfoil designs with human manager coordination.