Learning and Optimization with Bayesian Hybrid Models

2019-12-12 · Elvis A. Eugene, Xian Gao, Alexander W. Dowling

Bayesian hybrid models fuse physics-based insights with machine learning constructs to correct for systematic bias. In this paper, we compare Bayesian hybrid models against physics-based glass-box and Gaussian process black-box surrogate models. We consider ballistic firing as an illustrative case study for a Bayesian decision-making workflow. First, Bayesian calibration is performed to estimate model parameters. We then use the posterior distribution from Bayesian analysis to compute optimal firing conditions to hit a target via a single-stage stochastic program. The case study demonstrates the ability of Bayesian hybrid models to overcome systematic bias from missing physics with less data than the pure machine learning approach. Ultimately, we argue Bayesian hybrid models are an emerging paradigm for data-informed decision-making under parametric and epistemic uncertainty.

📄 PDF Abstract BibTeX arXiv:1912.06269

Code (0)

등록된 구현이 없습니다.

Tasks

BIG-bench Machine Learning Decision Making

Methods 이 논문이 사용한 방법론

Gaussian Process Gaussian Processes are non-parametric models for approximating functions. They rely upon a measure of similarity between points (the kernel function) to predict the value for…

Hybrid Parameter Search and Dynamic Model Selection for Mixed-Variable Bayesian Optimization

2022-06-03 · Hengrui Luo, Younghyun Cho, James W. Demmel, Xiaoye S. Li 외

This paper presents a new type of hybrid model for Bayesian optimization (BO) adept at managing mixed variables, encompassing both quantitative (continuous and integer) and qualitative (categorical) types. Our proposed n…

Bayesian Optimization Gaussian Processes Model Selection Position

Bayesian Optimization of Partially Known Systems using Hybrid Models

2026-03-11 · Eike Cramer, Luis Kutschat, Oliver Stollenwerk, Joel A. Paulson 외 arxiv

Bayesian optimization (BO) has gained attention as an efficient algorithm for black-box optimization of expensive-to-evaluate systems, where the BO algorithm iteratively queries the system and suggests new trials based o…

Hybrid Reinforcement Learning Framework for Mixed-Variable Problems

2024-05-30 · Haoyan Zhai, Qianli Hu, Jiangning Chen

Optimization problems characterized by both discrete and continuous variables are common across various disciplines, presenting unique challenges due to their complex solution landscapes and the difficulty of navigating …

Bayesian Optimization reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)+1

Hybrid methodology based on Bayesian optimization and GA-PARSIMONY to search for parsimony models by combining hyperparameter optimization and feature selection

2019-08-18 · Neurocomputing 2019 8 · F.J. Martinez-de-Pison, R. Gonzalez-Sendino, A Aldama, J. Ferreiro-Cabello 외

This article presents a hybrid methodology that combines Bayesian optimization (BO) with a constrained version of the GA-PARSIMONY method to obtain parsimony models. The proposal is designed to reduce the sizeable comput…

Bayesian Optimization feature selection Hyperparameter Optimization Model Selection

Bayesian Optimization over Hybrid Spaces

2021-06-08 · Aryan Deshwal, Syrine Belakaria, Janardhan Rao Doppa

We consider the problem of optimizing hybrid structures (mixture of discrete and continuous input variables) via expensive black-box function evaluations. This problem arises in many real-world applications. For example,…

Bayesian Optimization