Department of Mathematics, Florida State University (FSU)
Office: Love 311
Email: zecheng.zhang.math@gmail.com (FSU students: please contact me by my FSU email listed on the department of mathematics homepage)
NewsEducation and Working
I got my BSc from the Department of Mathematics at Hong Kong Baptist University. Later, I received full sponsorship to do a MSc (supervisors: Professor Yaushu Wong and Professor Peter Minev) in Mathematics at the Department of Mathematics at the University of Alberta.
I then did my Ph.D. in Mathematics under the supervise of Professor Yalchin Efendiev, and Professor Eric Chung, at Texas A&M University and graduated in 2021.
Following graduation, I joined the Department of Mathematics at Purdue as a visiting assistant professor under the supervise of Professor Guang Lin (Purdue).
Subsequently, I moved to the Department of Mathematics at Carneigie Mellon University and did postdoc with Professor Hayden Schaeffer (UCLA).
In August 2023, I joined the Department of Mathematics at Florida State University as an assistant professor.
Research Interests
Multiscale modeling and simulation
Mathematics of machine learning
Scientific machine learning
Editorial Board
Guest Editor of the MDPI Mathematics Special Issue “Trends and Prospects in Mathematical and Scientific Machine Learning”.
Jingmin Sun, Yuxuan Liu, Zecheng Zhang, Hayden Schaeffer. Towards a Foundation Model for Partial Differential Equation: Multi-Operator Learning and Extrapolation ArXiv preprint (2024).
Zecheng Zhang. MODNO: Multi Operator Learning With Distributed Neural Operators. ArXiv preprint (2024).
Christian Moya, Amirhossein Mollaali, Zecheng Zhang, Lu Lu and Guang Lin. Conformalized-DeepONet: A Distribution-Free Framework for Uncertainty Quantification in Deep Operator Networks. ArXiv preprint (2024).
Zecheng Zhang, Christian Moya, Lu Lu, Guang Lin and Hayden Schaeffer. D2NO: Efficient Handling of Heterogeneous Input Function Spaces with Distributed Deep Neural Operators. Computer Methods in Applied Mechanics and Engineering (2024).
Guang Lin, Na Ou, Zecheng Zhang, Zhidong Zhang. Restoring the Discontinuous Heat Equation Source Using Sparse Boundary Data and Dynamic Sensor. Inverse Problems (2024).
Yuxuan Liu, Zecheng Zhang, Hayden Schaeffer. PROSE: Predicting Operators and Symbolic Expressions using Multimodal Transformers. ArXiv preprint (2023).
Zecheng Zhang, Christian Moya, Wing Tat Leung, Guang Lin, Hayden Schaeffer. Bayesian deep operator learning for homogenized to fine-scale maps for multiscale PDE. Siam MMS (2024).
Zecheng Zhang, Wing Tat Leung, Hayden Schaeffer. A discretization-invariant extension and analysis of some deep operator networks. ArXiv preprint (2023).
Zecheng Zhang, Wing Tat Leung, Hayden Schaeffer. BelNet: Basis enhanced learning, a mesh-free neural operator. Proceedings Royal Society A: Mathematical, Physical and Engineering Sciences (2023).
A tutorial and programming code of BelNet and operator learning is here. This BelNet tutorial is on Kaggle and we will upload a tutorial on GitHub later.
Guanxun Li, Guang Lin, Zecheng Zhang, Quan Zhou. Fast Tempering for Stochastic Gradient Langevin Dynamics. ArXiv preprint (2023).
Na Ou, Zecheng Zhang, Guang Lin, A replica exchange preconditioned Crank-Nicolson Langevin
dynamic MCMC method for Bayesian inverse problems. Under the first round review of Journal of Computational Physic.
Yalchin Efendiev, Wing Tat Leung, Wenyuan Li, Zecheng Zhang. Hybrid explicit-implicit learning for multiscale problems with time dependent source. Communications in Nonlinear Science and Numerical Simulation (2023).
Guang Lin, Christian Moya, Zecheng Zhang. On Learning the Dynamical Response of Nonlinear Control Systems with Deep Operator Networks. Engineering Application of Artificial Intelligence (2023).
Guang Lin, Zecheng Zhang, Zhidong Zhang. Theoretical and numerical studies of inverse source problem for the linear parabolic equation with sparse boundary measurements. Inverse Problems (2022).
Guang Lin, Christian Moya, Zecheng Zhang. Accelerated replica exchange stochastic gradient Langevin diffusion enhanced Bayesian DeepONet for solving noisy parametric PDEs. Journal of Computational Physics (2022).
Yalchin Efendiev, Wing Tat Leung, Guang Lin, Zecheng Zhang. Efficient hybrid explicit-implicit learning for multiscale problems. Journal of Computational Physics (2022).
Wing Tat Leung, Guang Lin, Zecheng Zhang. NH-PINN: Neural homogenization based the physics-informed neural network for the multiscale problems. Journal of Computational Physics (2022).
Guang Lin, Yating Wang, Zecheng Zhang. Multi-variance replica exchange stochastic gradient MCMC for inverse and forward Bayesian physics-informed neural network. Journal of Computational Physics (2022).
Liu Liu, Tieyong Zeng, Zecheng Zhang. A deep neural network approach on solving the linear transport model under diffusive scaling. ArXiv preprint (2021).
Eric Chung, Yalchin Efendiev, Sai-Mang Pun, Zecheng Zhang. Computational multiscale methods for parabolic wave approximations in heterogeneous media. Applied Mathematics and Computation (2022).
Eric Chung, Yalchin Efendiev, Wing Tat Leung, Sai-Mang Pun and Zecheng Zhang. Multi-agent reinforcement learning aided sampling algorithms for a class of multiscale inverse problems. Journal of Scientific Computing (2023).
Boris Chetverushkin, Eric Chung, Yalchin Efendiev, Sai-Mang Pun and Zecheng Zhang. Computational multiscale methods for quasi-gas dynamic equations. Journal of Computational Physics (2020).
Eric Chung, Wing Tat Leung, Sai-Mang Pun and Zecheng Zhang. A multi-stage deep learning based algorithm for multiscale model reduction. Journal of Computational and Applied Mathematics (2020).
Eric Chung, Yalchin Efendiev, Wing Tat Leung, Zecheng Zhang. Learning Algorithms for Coarsening Uncertainty Space and Applications to Multiscale Simulations. Mathematics (2020).
