A Latent Variable Approach for Non-Hierarchical Multi-Fidelity Adaptive Sampling

TL;DR

This paper introduces a novel latent variable Gaussian process framework for non-hierarchical multi-fidelity adaptive sampling, improving efficiency and robustness in surrogate modeling and optimization by explicitly capturing inter-fidelity correlations.

Contribution

It proposes a non-hierarchical latent variable approach that models correlations among multiple fidelity levels, enabling more effective adaptive sampling without assuming hierarchical fidelity structures.

Findings

Outperforms benchmark methods in convergence rate and robustness.

Effectively captures inter-fidelity correlations without hierarchical assumptions.

Flexible to switch between global fitting and Bayesian optimization.

Abstract

Multi-fidelity (MF) methods are gaining popularity for enhancing surrogate modeling and design optimization by incorporating data from various low-fidelity (LF) models. While most existing MF methods assume a fixed dataset, adaptive sampling methods that dynamically allocate resources among fidelity models can achieve higher efficiency in the exploring and exploiting the design space. However, most existing MF methods rely on the hierarchical assumption of fidelity levels or fail to capture the intercorrelation between multiple fidelity levels and utilize it to quantify the value of the future samples and navigate the adaptive sampling. To address this hurdle, we propose a framework hinged on a latent embedding for different fidelity models and the associated pre-posterior analysis to explicitly utilize their correlation for adaptive sampling. In this framework, each infill sampling iteration includes two steps: We first identify the location of interest with the greatest potential improvement using the high-fidelity (HF) model, then we search for the next sample across all fidelity levels that maximize the improvement per unit cost at the location identified in the first step. This is made possible by a single Latent Variable Gaussian Process (LVGP) model that maps different fidelity models into an interpretable latent space to capture their correlations without assuming hierarchical fidelity levels. The LVGP enables us to assess how LF sampling candidates will affect HF response with pre-posterior analysis and determine the next sample with the best benefit-to-cost ratio. Through test cases, we demonstrate that the proposed method outperforms the benchmark methods in both MF global fitting (GF) and Bayesian Optimization (BO) problems in convergence rate and robustness. Moreover, the method offers the flexibility to switch between GF and BO by simply changing the acquisition function.