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Found 4 papers, 2 papers with code
Personalized Autonomous Driving with Large Language Models: Field Experiments
We also validate that the proposed memory module considers personalized preferences and further reduces the takeover rate by up to 65. 2% compared with those without a memory module.
Learning Arbitrary Quantities of Interest from Expensive Black-Box Functions through Bayesian Sequential Optimal Design
We model the underlying information source as a fully-Bayesian, non-stationary Gaussian process (FBNSGP), and derive an approximation of the information gain of a hypothetical experiment about an arbitrary QoI conditional on the hyper-parameters The EIG about the same QoI is estimated by sample averages to integrate over the posterior of the hyper-parameters and the potential experimental outcomes.
Simulator-free Solution of High-Dimensional Stochastic Elliptic Partial Differential Equations using Deep Neural Networks
We propose a novel methodology for high-dimensional uncertainty propagation of elliptic SPDEs which lifts the requirement for a deterministic forward solver.
Data Analysis, Statistics and Probability Computational Physics
Bayesian Optimal Design of Experiments For Inferring The Statistical Expectation Of A Black-Box Function
Our hypothesis is that an optimal BODE should be maximizing the expected information gain in the QoI.
