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TAB 为热分析领域研究人员提供一个明确的、可重复的方法对比平台,从而实现方法的评估并促进热分析方法的整体研究。此外,TAB 通过较好的建模,降低其他学科研究人员研究该问题的门槛,吸引更多的人投入该领域进行相关研究。

引用 TAB 基准

如果在论文中使用 TAB 基准,请引用相应热分析基准的论文以及其他相关的基于该基准的论文。

热布局预测基准
@article{chen_hsld,
    Author = {Xianqi Chen and Xiaoyu Zhao and Zhiqiang Gong  and Jun Zhang and Xiaoqian Chen and Wen Yao},
    Title = {A Deep Neural Network Surrogate Modeling Benchmark for Temperature Field Prediction of Heat Source Layout},
    Journal = {Science China-Physics, Mechanics & Astronomy},
    Year = {2021}
}
        
@article{zhao2021,
    Author = {Xiaoyu Zhao and Zhiqiang Gong and Jun Zhang and Wen Yao and Xiaoqian Chen},
    Title = {A surrogate model with data augmentation and deep transfer learning for temperature field prediction of heat source layout},
    Journal = {Structural and Multidisciplinary Optimization},
    Year = {2021}
}
        
温度场重建基准
@article{gong_tfrd,
    Author = {Xiaoqian Chen and Zhiqiang Gong and Xiaoyu Zhao and Weien Zhou and Wen Yao},
    Title = {A Machine Learning Modelling Benchmark for Temperature Field Reconstruction of Heat-Source Systems},
    Journal = {arXiv preprint arXiv:2108.08298},
    Year = {2021}
}
        
@article{gong_pirl,
    Author = {Zhiqiang Gong and Weien Zhou and Jun Zhang and Wei Peng and Wen Yao},
    Title = {Physics-Informed Deep Reversible Regression Model for Temperature Field Reconstruction of Heat-Source Systems},
    Journal = {arXiv preprint arXiv:2106.11929},
    Year = {2021}
}
        

注意事项

比赛信息
CEC2022 HCLO竞赛已经顺利结束!