I am now working with Prof. Petros Koumoutsakos at Harvard University. I obtained my Ph.D. degree in Aerospace and Mechanical Engineering at University of Notre Dame (2018 summer to 2023 summer) advised by Prof. Jian-Xun Wang. I am also very fortunate to work with Prof. Matthew J. Zahr. I received my master’s degree in Mechanical Engineering & Materials Science (MEMS) at Washington University in St. Louis (WUSTL) advised by Prof. Ramesh Agarwal and bachelor’s degree in Shanghai University of Electric Power where I was having fun. 科研好像流沙 我不掙扎
Han Gao
hgao1@seas.harvard.edu
Applied Mathematics at Harvard School of Engineering
Publications
Journal papers-
Physics-informed graph neural Galerkin networks: A unified framework for solving PDE-governed forward and inverse problems (Computer Methods in Applied Mechanics and Engineering) Han Gao, Matthew J Zahr, Jian-Xun Wang
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Super-resolution and denoising of fluid flow using physics-informed convolutional neural networks without high-resolution labels (Physics of Fluids) Han Gao, Jian-Xun Wang
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A Bi-fidelity ensemble kalman method for PDE-constrained inverse problems in computational mechanics (Computational Mechanics) Han Gao, Jian-Xun Wang
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PhyGeoNet: Physics-informed geometry-adaptive convolutional neural networks for solving parameterized steady-state PDEs on irregular domain (Journal of Computational Physics) Han Gao, Luning Sun, Jian-Xun Wang
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Non-intrusive model reduction of large-scale, nonlinear dynamical systems using deep learning (Physica D: Nonlinear Phenomena) Han Gao, Jian-Xun Wang, Matthew J Zahr
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A bi-fidelity surrogate modeling approach for uncertainty propagation in three-dimensional hemodynamic simulations (Computer Methods in Applied Mechanics and Engineering) Han Gao, Xueyu Zhu, Jian-Xun Wang
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Surrogate modeling for fluid flows based on physics-constrained deep learning without simulation data (Computer Methods in Applied Mechanics and Engineering) Luning Sun, Han Gao, Shaowu Pan, Jian-Xun Wang
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Surrogate modeling for fluid flows based on physics-constrained deep learning without simulation data (Computer Methods in Applied Mechanics and Engineering) Luning Sun, Han Gao, Shaowu Pan, Jian-Xun Wang
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Study of Round Jet Impingement in Proximity of Ground and Water Surface (Journal of Aircraft) Han Gao, Quanzhong Liu, Qiulin Qu, Ramesh K Agarwal
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Unifying Predictions of Deterministic and Stochastic Physics in Mesh-reduced Space with Sequential Flow Generative Model (NeurIPS 2023) Luning Sun, Xu Han, Han Gao, Jian-Xun Wang, Liping Liu
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PatchGT: Transformer Over Non-Trainable Clusters for Learning Graph Representations (Proceedings of the First Learning on Graphs Conference) Han Gao (co-first), Xu Han (co-first), Jiaoyang Huang, Jian-Xun Wang, Liping Liu
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Predicting Physics in Mesh-reduced Space with Temporal Attention (ICLR 2022) Xu Han (co-first), Han Gao (co-first), Tobias Pfaff, Jian-Xun Wang, Liping Liu
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Numerical Investigation of a Submerged Water Jet Impinging at Various Angles on Ground (AIAA Scitech 2020 Forum) Xiang Zhang, Ramesh K. Agarwal, Han Gao and Ling Zhou
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Numerical Simulation of Fountain formation due to Twin-Jet impingement on Ground (AIAA Scitech 2019 Forum) Xiang Zhang, Ramesh K. Agarwal, Han Gao and Ling Zhou
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Numerical Simulation of the Cavitation Flow around a Hydrofoil Based on a Coupled CFD-PBM Model (AIAA Scitech 2019 Forum) Quanzhong Liu, Kefan Xu, Han Gao and Ramesh K. Agarwal
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Numerical Study of a Hovering Helicopter Rotor Blade in Ground Effect (AIAA Scitech 2019 Forum) Han Gao and Ramesh K. Agarwal
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Numerical Investigation of Cavitation Characteristics of a Liquid Oxygen Turbo Pump (AIAA Aviation 2019 Forum) Quanzhong Liu, Lujia Gong, Han Gao and Ramesh K. Agarwal
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Numerical Study of Round Jet Impingement in Proximity of Ground and a Water Surface (AIAA Aviation 2018 Forum) Han Gao, Quanzhong Liu, Qiulin Qu and Ramesh K. Agarwal
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CFD Simulations of Two Rectangular Water Jets Impinging on a Water Pool (AIAA Aviation 2018 Forum) Han Gao, Xu Han, Quanzhong Liu and Ramesh K. Agarwal