A curated paper list of existing Multi-fidelity Fusion for
Electronic Design Automation (EDA) studies.
The list is under construction.
Check out How to contribute & add my publications?
Awesome Multi-fidelity Fusion including the projects and work done by our team in the field of FidelityFusion
By leveraging the multi-fidelity data, the surrogate model can be trained with many low-fidelity data, which is cheap to generate, and a few high-fidelity data to predict the output of the high-fidelity simulation accurately.
FidelityFusion focus on tractable multi-fidelity fusion methods, which can be easily optimized and scaled to high-dimensional output with strong generalization and robustness.
Awesome Multi-fidelity Fusion
# pub.yaml
categories_publications:
name: "All publications"
categories:
-
heading: "Multi fidelity Fusion"
file: Multi-fidelity_Fusion.bib
-
heading: "Bayesian optimization"
file: Bayesian_optimization.bib
-
heading: "Uncertainty analysis"
file: uncertainty_analysis.bib
-
heading: "Surrogate modeling"
file: surrogate_modeling.bib
FidelityFusion was developed and maintained by mainly by Wei. W. Xing at IceLab-X and Zen Xing at Rockchips. A non-exhaustive but growing list needs to mention: Yuxing Wang and Guanjie Wang at BUAA.
Please cite our paper if you find it helpful :)
@inproceedings{
wang2022gar,
title=: Generalized Autoregression for Multi-Fidelity Fusion},
author={Yuxin Wang and Zheng Xing and WEI W. XING},
booktitle={Advances in Neural Information Processing Systems},
editor={Alice H. Oh and Alekh Agarwal and Danielle Belgrave and Kyunghyun Cho},
year={2022},
url={https://openreview.net/forum?id=aLNWp0pn1Ij}
}