Goal-Oriented Bayesian Optimal Experimental Design for Nonlinear Models using Markov Chain Monte Carlo
Predictive goal-oriented OED seeks to maximize the expected information gain on quantities of interest, distinct from traditional parameter-focused OED.
Scalable and Efficient Bayesian Optimal Experimental Design with Derivative-Informed Neural Operators
The core message of this article is to develop an accurate, scalable, and efficient computational framework for Bayesian optimal experimental design (OED) problems by leveraging derivative-informed neural operators (DINOs). The proposed method addresses the key challenges in Bayesian OED, including the high computational cost of evaluating the parameter-to-observable (PtO) map and its derivative, the curse of dimensionality in the parameter and experimental design spaces, and the combinatorial optimization for sensor selection.
Bayesian Optimal Experimental Design Using a Surrogate Model for Parameter Estimation in Chromatography with the Equilibrium Dispersive Model
This paper presents a computationally efficient method for Bayesian optimal experimental design (BOED) in chromatography, using a surrogate model to reduce the computational cost associated with solving the Equilibrium Dispersive Model (EDM).
크로마토그래피에서 베이지안 최적 실험 설계를 위한 대리 모델 활용
본 논문에서는 크로마토그래피의 평형 분산 모델(EDM)에서 매개변수 추정의 효율성을 향상시키기 위해 Piecewise Sparse Linear Interpolation(PSLI) 기반 대리 모델을 활용한 베이지안 최적 실험 설계 방법론을 제시합니다.
A Novel Approach to Bayesian Optimal Experimental Design Using Contrastive Diffusions
This paper introduces a novel, computationally efficient method for Bayesian Optimal Experimental Design (BOED) that leverages contrastive diffusions and a new concept called the "expected posterior distribution" to maximize information gain from experiments, particularly in high-dimensional settings and with generative models.