Pan Du杜攀
Exploring AI for Computational Physics & Health探索人工智能赋能计算物理与健康科学
About简介
I am an incoming Assistant Professor in the Department of Mechanical Engineering at Iowa State University, where I will establish the Physics & Health via AI and Shape Emulation (PHASE) Lab beginning in Fall 2027. My research focuses on scientific machine learning for computational physics and geometry modeling.
我将于 2027 年秋季加入爱荷华州立大学（Iowa State University）机械工程系担任助理教授，并创立 PHASE Lab（Physics & Health via AI and Shape Emulation）。实验室致力于人工智能、计算物理与几何建模的交叉研究。
I received my undergraduate degree from Tsinghua University and my master's degree from Washington University in St. Louis under Prof. Ramesh Agarwal. I earned my Ph.D. in Aerospace and Mechanical Engineering from the University of Notre Dame under the supervision of Prof. Jian-Xun Wang, and I am currently a postdoctoral researcher at the University of California, Berkeley with Prof. Shawn Shadden.
我本科毕业于清华大学，随后在圣路易斯华盛顿大学师从 Ramesh Agarwal 教授获得硕士学位。我在圣母大学航空航天与机械工程专业师从 Jian-Xun Wang 教授获得博士学位，目前在加州大学伯克利分校 Shawn Shadden 教授课题组从事博士后研究。
My research integrates AI, CFD, and geometric modeling to enable scalable many-query analysis and digital twins for PDE-governed systems, with applications ranging from cardiovascular biomechanics to broader multi-physics problems. Moving forward, my PHASE lab aims to push the boundaries of AI and geometry modeling technologies to advance innovation in physical and biomedical sciences.
我的研究融合人工智能（AI）、计算流体力学（CFD）与几何建模，致力于构建面向复杂偏微分方程（PDE）系统的智能计算框架，支持大规模参数分析、优化设计、不确定性量化以及数字孪生技术。相关方法已应用于心血管生物力学研究，并可进一步推广至更广泛的多物理场问题。未来，PHASE Lab 将持续探索人工智能与几何建模技术的前沿发展方向，推动物理科学与生物医学科学领域的交叉创新。
Research projects研究项目
Generative modeling for vascular geometry血管几何的生成式AI建模
Cross-patient shape registration跨患者形状配准
Differentiable modeling for fluid mechanics流体力学可微分计算模型
Generative AI for turbulence modeling面向湍流建模的生成式 AI
Semi-automatic image segmentation半自动AI医学图像分割
Large-scale computational physics大规模计算物理
Contact联系方式
Email:邮箱： [email protected]
Phone:电话： 5748009663
Affiliation:单位： University of California, Berkeley加州大学伯克利分校 2167 Etcheverry Hall, Berkeley, CA 94720
Upcoming events近期动态
- I will be joining Department of Mechanical Engineering at Iowa State University as a tenure-track assistant professor in Fall 2027.我将于 2027 年秋季加入爱荷华州立大学机械工程系，担任终身教职序列助理教授。
- Downstream application for the DiFVM solver is on the way.DiFVM 求解器的下游应用正在开发中。
Past events过往动态
- (April 2026) Our GPU enabled solver DiFVM is online: （2026年4月）我们的 GPU 加速可微流体模拟框架 DiFVM 已正式发布： arXiv:2603.15920.
- (Feb 2026) I will be joining Dr. Shawn Shadden's lab at University of California, Berkeley starting Feb 9.（2026 年 2 月）我将于 2 月 9 日起加入加州大学伯克利分校 Shawn Shadden 教授课题组。
- (Jan 2026) I will defend my Ph.D. dissertation on the topic From Medical Imaging to Hemodynamics: AI-Enabled Modeling of Cardiovascular Flow.（2026 年 1 月）我将进行博士论文答辩，论文题目为 From Medical Imaging to Hemodynamics: AI-Enabled Modeling of Cardiovascular Flow。
- (Nov 2025) I will be attending APS DFD as chair host for Biological Fluid Dynamics: Cardiac Flow II. I will also present our latest GPU-enabled solver titled "Differentiable Graph-Based Finite Volume Solver for Patient-Specific Cardiovascular Flow Simulation"（2025 年 11 月）我将参加 APS DFD，并担任 Biological Fluid Dynamics: Cardiac Flow II 分会主持人。同时，我将报告我们最新的 GPU 加速计算模型工作：Differentiable Graph-Based Finite Volume Solver for Patient-Specific Cardiovascular Flow Simulation。
- (Sep 2025) I am entering the academic job market this year for faculty positions focused on scientific machine learning, computational fluid dynamics, and cardiovascular flow.（2025 年 9 月）我今年进入学术职位市场，研究方向聚焦科学机器学习、计算流体力学与心血管流动。
- (Jul 2025) I will be attending USNCCM18 on July 20 at Chicago! See you guys there!（2025 年 7 月）我将于 7 月 20 日在芝加哥参加 USNCCM18，期待见到大家！
- (Jul 2025) I will be teaching course AME 40411: Introduction to Artificial Intelligence this fall at the University of Notre Dame.（2025 年 7 月）我将在今年秋季于圣母大学讲授课程 AME 40411：人工智能导论。
Publications论文发表
Preprints预印本
- Du, P., Xu, M., Li, Y., & Wang, J. X. (2026). DiFVM: A Vectorized Graph-Based Finite Volume Solver for Differentiable CFD on Unstructured Meshes. arXiv:2603.15920.
- Du, P., Xu, M., Zhu X., & Wang, J. X. (2026). HUG-VAS: A Hierarchical NURBS-Based Generative Model for Aortic Geometry Synthesis and Controllable Editing. arXiv:2507.11474.
Published已发表
- Guo, J., Du, P., & Wang, J. X. (2026). Conditional Neural Field for Spatial Dimensionality Reduction of Turbulence Data: A Comparison Study. Physics of Fluids, 38(2).
- Du, P., An, D., Wang, C., & Wang, J. X. (2025). AI-Powered Automated Model Construction for Patient-Specific CFD Simulations of Aortic Flows. Science Advances, 11(36), eadw2825.
- Zhou, J., Li, R., Shang, W., Liu, Y, Panda, J. P., Du, P., Liu X., Liang J., Ma, B., Wang, J. X., & Luo, T. (2025). Physics-Informed Neural Networks with Hard-Encoded Angle-Dependent Boundary Conditions for Phonon Boltzmann Transport Equation. Materials Today Physics, 101922.
- An, D., Du, P., Wang, J. X., & Wang C. (2025). AortaDiff: Volume-Guided Conditional Diffusion Models for Multi-Branch Aortic Surface Generation. IEEE Transactions on Visualization and Computer Graphics.
- Shang, W., Zhou, J., Panda, J. P., Xu, Z., Liu, Y., Du, P., Wang J. X., & Luo, T. (2025). JAX-BTE: A GPU-Accelerated Differentiable Solver for Phonon Boltzmann Transport Equations. npj Computational Materials, 11(1), 129.
- Akhare, D., Du, P., Luo, T., & Wang, J. X. (2025). Implicit Neural Differential Model for Spatiotemporal Dynamics. Computer Methods in Applied Mechanics and Engineering, 446(Part B), 118280. https://doi.org/10.1016/j.cma.2025.118280.
- An, D., Du, P., Gu, P., Wang, J. X., & Wang, C. (2025). Hierarchical LoG Bayesian Neural Network for Enhanced Aorta Segmentation. In 2025 IEEE 22nd International Symposium on Biomedical Imaging (ISBI) (pp. 1-5). IEEE.
- Corpuz, A. M., Jaiswal, M., Du, P., Ramachandra, A. B., Wang, J. X., & Hsu, M. C. (2025). Direct medical image to simulation using auto-segmentation and point cloud-based CFD. Advances in Computational Science and Engineering, 3, 95-124.
- Du, P., Parikh, M. H., Fan, X., Liu, X. Y., & Wang, J. X. (2024). Conditional neural field latent diffusion model for generating spatiotemporal turbulence. Nature Communications, 15(1), 10416.
- Prashanth, M., Du, P., Wang, J. X., & Wu, H. (2024). A neural network-based algorithm for the reconstruction and filtering of single particle trajectory in magnetic particle tracking. Review of Scientific Instruments, 95(5).
- Liu, X. Y., Parikh, M. H., Fan, X., Du, P., Wang, Q., Chen, Y. F., & Wang, J. X. (2024). CoNFiLD-inlet: Synthetic Turbulence Inflow Using Generative Latent Diffusion Models with Neural Fields. arXiv preprint arXiv:2411.14378. Accepted by Physical Review Fluids.
- Du, P., & Wang, J. X. (2022). Reducing Geometric Uncertainty in Computational Hemodynamics by Deep Learning-Assisted Parallel-Chain MCMC. Journal of Biomechanical Engineering, 144(12), 121009.
- Du, P., Zhu, X., & Wang, J. X. (2022). Deep learning-based surrogate model for three-dimensional patient-specific computational fluid dynamics. Physics of Fluids, 34(8).
- Prashanth, M. N., Du, P., Wang, J. X., & Wu, H. (2022). AI-based Hybrid Model for Denoising Particle Trajectories Reconstructed from Magnetic Particle Tracking Method. In AIAA SCITECH 2022 Forum (p. 1162).
- Sun, L., Du, P., Sun, H., & Wang, J. X. (2022). Group sparse Bayesian learning for data-driven discovery of explicit model forms with multiple parametric datasets. Numerical Algebra, Control and Optimization.
- Wu, H., Du, P., Kokate, R., & Wang, J. X. (2021). A semi-analytical solution and AI-based reconstruction algorithms for magnetic particle tracking. PLOS ONE, 16(7), e0254051.
- Wang, X., Yablonsky, G. S., ur Rahman, Z., Yang, Z., Du, P., Tan, H., & Axelbaum, R. L. (2021). Assessment of sulfur trioxide formation due to enhanced interaction of nitrogen oxides and sulfur oxides in pressurized oxy-combustion. Fuel, 290, 119964.
- Du, P., & Agarwal, R. K. (2019). Numerical drag prediction of NASA Common Research Models using different turbulence models. Computers & Fluids, 191, 104238.
- Wang, X., Adeosun, A., Yablonsky, G., Gopan, A., Du, P., & Axelbaum, R. L. (2018). Synergistic SO x/NO x chemistry leading to enhanced SO 3 and NO 2 formation during pressurized oxy-combustion. Reaction Kinetics, Mechanisms and Catalysis, 123, 313-322.
- Du, P., & Agarwal, R. K. (2017). Drag Prediction of NASA Common Research Models Using Different Turbulence Models. 35th AIAA Applied Aerodynamics Conference.
PhD thesis博士论文
- Du, P., (2026). From Medical Imaging to Hemodynamics: AI-Enabled Modeling of Cardiovascular Flow. University of Notre Dame.
AME40411 Introduction to Artificial IntelligenceAME40411 人工智能导论
Course Instructor, undergraduate level, Fall 2025, 3 credits.课程教师，本科生课程，2025 年秋季，3 学分。
Download syllabus下载课程大纲My teaching philosophy emphasizes integrating modern AI methods with physical intuition and engineering principles. I aim to help students develop both theoretical understanding and practical problem-solving skills for next-generation computational engineering.
我的教学理念是将现代人工智能方法与物理直觉和工程原理有机结合，帮助学生建立扎实的理论基础，并培养解决实际问题的能力，为下一代计算工程的发展做好准备。
We are currently hiring highly motivated PhD students, please see details in the hiring ad below.我们正在招收积极主动的博士生，具体信息请见下方招生广告。
Download Recruitment Flyer下载招生海报