# A support vector regression-based multi-fidelity surrogate model

arXiv: https://arxiv.org/abs/1906.09439 · HTML: https://mlsanity.com/paper/1906.09439/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Jun 2019.

## Built on by (most cited first, 12 of 12)

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- [GAN-MDF: A Method for Multi-fidelity Data Fusion in Digital Twins](https://mlsanity.com/paper/2106.14655/) (2021)
- [Digital Twin and Artificial Intelligence Incorporated With Surrogate Modeling for Hybrid and Sustainable Energy Systems](https://mlsanity.com/paper/2210.00073/) (2022)
- [An Efficient Hierarchical Kriging Modeling Method for High-dimension Multi-fidelity Problems](https://mlsanity.com/paper/2301.00216/) (2022)
- [Characterising harmful data sources when constructing multi-fidelity surrogate models](https://mlsanity.com/paper/2403.08118/) (2024)
- [A Surrogate-Augmented Symbolic CFD-Driven Training Framework for Accelerating Multi-objective Physical Model Development](https://mlsanity.com/paper/2512.19031/) (2025)
- [A Unified Hierarchical Multi-Task Multi-Fidelity Framework for Data-Efficient Surrogate Modeling in Manufacturing](https://mlsanity.com/paper/2603.09842/) (2026)
- [Benchmarking Tabular Foundation Models as Surrogates in Expensive Evolutionary Optimization](https://mlsanity.com/paper/2609.18130/) (2026)

## Abstract (arXiv)

Computational simulations with different fidelity have been widely used in engineering design. A high-fidelity (HF) model is generally more accurate but also more time-consuming than an low-fidelity (LF) model. To take advantages of both HF and LF models, multi-fidelity surrogate models that aim to integrate information from both HF and LF models have gained increasing popularity. In this paper, a multi-fidelity surrogate model based on support vector regression named as Co_SVR is developed by combining HF and LF models. In Co_SVR, a kernel function is used to map the map the difference between the HF and LF models. Besides, a heuristic algorithm is used to obtain the optimal parameters of Co_SVR. The proposed Co_SVR is compared with two popular multi-fidelity surrogate models Co_Kriging model, Co_RBF model, and their single-fidelity surrogates through several numerical cases and a pressure vessel design problem. The results show that Co_SVR provides competitive prediction accuracy for numerical cases, and presents a better performance compared with the Co_Kriging and Co_RBF models and single-fidelity surrogate models.