Papers

[1] Gong Z, Zhou W, Zhang J, et al. Physics-Informed Deep Reversible Regression Model for Temperature Field Reconstruction of Heat-Source Systems[J]. arXiv preprint arXiv:2106.11929, 2021.

[2] Chen X, Gong Z, Zhao X, et al. A Machine Learning Modelling Benchmark for Temperature Field Reconstruction of Heat-Source Systems[J]. arXiv preprint arXiv:2108.08298, 2021.

The Intelligent Design and Robust Learning laboratory of Defense Innovation Institute of Academy of Military Sciences focuses on the basic frontier and cross-research of artificial intelligence and aircraft design. The team has developed a series of data and model driven deep learning methods for efficient multi-disciplinary analysis and optimization of aircraft, and tries to construct a intelligent auxiliary aircraft design optimization cloud platform, namely IDaaS （Intelligent Design as a Service）. The team has already publicly released two thermal analysis benchmarks, namely the HSLD （Heat-source layout dataset） and TFRD （Temperature field reconstruction dataset）. Besides, they developed a physical informed neural network framework IDRLNet. In the past three years, they have published more than 30 related papers, and more than 30 patent applications.

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