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Wenzhuo Xu

wzxu [at] cmu [dot] edu

I am a PhD candidate in the Department of Mechanical Engineering at Carnegie Mellon University. I am fortunate to be advised by Prof. Christopher McComb and Prof. Noelia Grande Gutiérrez. Also check out my fellow group members of Design Research Collective and BiosiMMlab. My research interests lie in the intersection of machine learning and computational fluid dynamics, with a focus on developing data-driven methods for the super-resolution of multi-scale flows.

Education

  • Ph.D. in Mechanical Engineering, Carnegie Mellon University, 2022 - present
  • B.Eng. in Mechanical Engineering, Shanghai Jiao Tong University, 2018 - 2022
  • B.A. in German, Shanghai Jiao Tong University, 2018 - 2022

Publications

  • Wenzhuo Xu, Noelia Grande Gutierrez, and Christopher McComb. “MegaFlow2D: A Parametric Dataset for Machine Learning Super-resolution in Computational Fluid Dynamics Simulations.” In Proceedings of Cyber-Physical Systems and Internet of Things Week 2023 (CPS-IoT Week ‘23). [ACM]
  • Chen, Jiangce, Wenzhuo Xu, Martha Baldwin, Björn Nijhuis, Ton van den Boogaard, Noelia Grande Gutiérrez, Sneha Prabha Narra, and Christopher McComb. “Capturing Local Temperature Evolution during Additive Manufacturing through Fourier Neural Operators.” IDETC-CIE 2023 (2023). [arXiv]
  • Chen, Jiangce, Wenzhuo Xu, Martha Baldwin, Björn Nijhuis, Ton van den Boogaard, Noelia Grande Gutiérrez, Sneha Prabha Narra, and Christopher McComb. “Capturing Local Temperature Evolution during Additive Manufacturing through Fourier Neural Operators.” Journal of Manufacturing Science and Engineering (2024). [ASME]
  • Wenzhuo Xu, Christopher McComb, and Noelia Grande Gutierrez. “Taylor Series Error Correction Network for Super-Resolution of Discretized Partial Differential Equation Solutions” Journal of Computational Physics (2024).

Conference & Invited talks

  • “Taylor series error correction network for super-resolution of discretized fluid solutions”, APS-DFD 2023, Washington DC, Nov 2023.
  • “Adaptive Local Domain Decomposition for Learning Large-Scale Multi-physics Numerical Simulations”, APS-DFD 2024, Salt Lake City, UT, Nov 2024.
  • “Machine Learning in Large-Scale Engineering Simulations”, Invited talk at Autodesk Research, Nov 2024.