Characterizing Harmful Data Sources in Multi-Fidelity Surrogate Modeling

Core Concepts

Characterizing harmful data sources is crucial in determining when to use low-fidelity data sources in surrogate modeling for industrial design problems.

Abstract

Surrogate modeling techniques are essential for costly design assessments. Recent studies focus on characterizing harmful data sources to guide model construction. Instance Space Analysis provides insights into when to rely on low-fidelity sources. SVM predictions aid in algorithm selection based on performance analysis. Surrogate models like Kriging and Co-Kriging are widely used for multi-fidelity problems. The study compares their accuracy using benchmark filtering techniques. The research aims to provide guidelines for practitioners in industrial settings. The study uses a diverse set of function pairs, including literature-based and disturbance-based instances, along with simulations from the SOLAR engine. SVMs predict the performance of Kriging and Co-Kriging models based on selected features. Analysis reveals that relative sample budgets play a significant role in model performance prediction. Features related to high-fidelity data availability show strong correlations with algorithm performance. Instance Space Analysis aids in understanding when to utilize low-fidelity sources effectively.

Stats

Recent studies have focused on characterizing harmful data sources for guiding practitioners. The feature Br has a correlation of 0.483 with Kriging performance, indicating its importance. Budget features relative to problem dimension show strong correlations with algorithm performance.

Quotes

Key Insights Distilled From

Characterising harmful data sources when constructing multi-fidelity surrogate models

by Nico... at arxiv.org 03-14-2024

https://arxiv.org/pdf/2403.08118.pdf

Deeper Inquiries

How can the findings of this study be applied practically in industrial design settings

The findings of this study can be applied practically in industrial design settings by providing guidelines on when to use low-fidelity sources when constructing surrogate models. The analysis conducted in the study helps identify regions in the instance space where using a low-fidelity source is beneficial and where it may be harmful. This information can guide practitioners in making informed decisions about whether to incorporate low-fidelity data into their modeling process based on the characteristics of the available data sources. By following these guidelines, industrial designers can optimize their model construction process, leading to more accurate predictions and efficient utilization of resources.

What potential biases could arise from relying on synthetic test instances for model assessment

Relying solely on synthetic test instances for model assessment could introduce several potential biases. One bias could arise from the creation procedures used to generate synthetic high- and low-fidelity function pairs, as they may not accurately reflect real-world scenarios or variations present in actual data sources. Additionally, biases could stem from limited diversity in the types of instances included in the synthetic test suite, leading to an incomplete representation of possible scenarios that may occur during model training and evaluation. Furthermore, assumptions made during the generation of synthetic instances may not fully capture all complexities present in practical applications, potentially skewing results and recommendations derived from such assessments.

How can the concept of instance space analysis be extended to other fields beyond surrogate modeling

The concept of instance space analysis can be extended beyond surrogate modeling to other fields that involve algorithmic performance evaluation across diverse sets of instances. For example: In machine learning: Instance space analysis could be applied to compare different algorithms' performance across various datasets or problem domains. In optimization: Instance space analysis could help analyze how optimization algorithms perform under different problem landscapes or constraints. In simulation studies: Instance space analysis could assist researchers in understanding how simulation models behave across a range of input parameters or conditions. By applying instance space analysis techniques outside surrogate modeling, researchers and practitioners can gain valuable insights into algorithm behavior and performance variability across diverse sets of instances relevant to their respective fields.