Baiming Zhang 张柏铭

I'm a senior undergraduate in Engineering Mechanics at Zhejiang University, working under the guidance of Prof. Shiying Xiong. My research lies at the intersection of scientific machine learning and scientific computing, focusing on neural operators and computational methods for PDEs, with applications to fluid systems, Schrödinger-type models, and reduced-order modeling.

I am fortunate to collaborate with Mengdi Wang, a postdoctoral fellow in the PhysAI Lab at the University of British Columbia, and Prof. Peter Yichen Chen, who directs the lab. I am now registered as a Visiting International Research Student (VIRS) at UBC and will spend four months conducting research there.

Previously, I worked in the Zhejiang Key Laboratory of Micro-Nano Quantum Chips and Quantum Control on quantum and atomic physics related topics, including Rydberg atoms, Raman effects, EIT, laser frequency stabilization, and related theoretical modeling, under the supervision of Prof. Dawei Wang and Prof. Xingqi Xu.

Baiming Zhang profile photo

News

  • [2026.10]NEB was released on arXiv.
  • [2026.10]Orbit Workspace is now available as an open-source project. Welcome to try it! 😊
  • [2026.09]Ranked 1st by GPA in my major at Zhejiang University for the 2025–2026 academic year.
  • [2026.09]LGNO was released on arXiv.
  • [2026.09]LatentDDM was released on arXiv.

Publications

Authors with * are corresponding authors.
Neural Eigenfluid Bases overview Neural Eigenfluid Bases: Spectral Objectives, Gauge, and Topology
Baiming Zhang, Shiying Xiong, Zhecheng Wang*
arXiv 2026

Neural eigenfluid bases provide continuous, physically constrained representations for reduced-order fluid simulation, connecting spectral objectives, gauge fixing, and topology.

Local gradient neural operator overview Local gradient neural operator
Baiming Zhang, Jinsong Tang*, Ying Xu*, Lihua Chen, Shiying Xiong
arXiv 2026

A lightweight local-to-global framework learns PDE solution operators from compact local representations, aiming for efficient prediction with improved stability and interpretability.

Learnable Composition for Neural Operators overview Learnable composition for neural operators
Zituo Chen, Baiming Zhang, Sili Deng*
NeurIPS 2026 AI for Science Workshop

LatentDDM pairs pretrained local neural operators with a lightweight learnable composition module, enabling efficient transfer to larger domains and previously unseen operating conditions.

Education

Zhejiang University emblem Zhejiang University
Sep 2023 – Jun 2027 (expected)
Bachelor's degree, Engineering Mechanics
University of British Columbia
Oct 2026 – Jan 2027 (expected)
Visiting International Research Student

Awards

Excellent oral presentation at the Mechanics 101 Educational Initiative undergraduate academic forum Excellent Oral Presentation Award, Mechanics “101 Educational Initiative” Undergraduate Academic Forum, May 2026
World Gold Medal award ceremony World Gold Medal! (Top 0.1%), International College Student Engineering Mechanics Competition, Apr 2025
  • Chinese National Zhou Peiyuan Mechanics Competition, First Prize, Nov 2025
  • Zhejiang Provincial Government Scholarship, 2023–2024 and 2024–2025
  • China Aerospace Scholarship, 2024–2025
  • Chu Kochen Honors College Scholarship for Outstanding Students, 2024–2025
  • Mathematics Competition for College Students, First Prize, Nov 2023

Collaboration

I am open to any academic collaboration related to fluid dynamics, scientific machine learning, neural operators, and physical modeling. Please feel free to contact me!