Zecheng Zhang

According to our database, Zecheng Zhang authored at least 56 papers between 2019 and 2025.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2025

A discretization-invariant extension and analysis of some deep operator networks.

Zecheng Zhang, Wing Tat Leung, Hayden Schaeffer

J. Comput. Appl. Math., 2025

2024

PROSE: Predicting Multiple Operators and Symbolic Expressions using multimodal transformers.

Yuxuan Liu, Zecheng Zhang, Hayden Schaeffer

Neural Networks, 2024

Bayesian Deep Operator Learning for Homogenized to Fine-Scale Maps for Multiscale PDE.

Zecheng Zhang, Christian Moya, Wing Tat Leung, Guang Lin, Hayden Schaeffer

Multiscale Model. Simul., 2024

A replica exchange preconditioned Crank-Nicolson Langevin dynamic MCMC method with multi-variance strategy for Bayesian inverse problems.

Na Ou, Zecheng Zhang, Guang Lin

J. Comput. Phys., 2024

ContextGNN: Beyond Two-Tower Recommendation Systems.

Yiwen Yuan, Zecheng Zhang, Xinwei He, Akihiro Nitta, Weihua Hu, Dong Wang, Manan Shah, Shenyang Huang, Blaz Stojanovic, Alan Krumholz, Jan Eric Lenssen, Jure Leskovec, Matthias Fey

CoRR, 2024

DeepONet as a Multi-Operator Extrapolation Model: Distributed Pretraining with Physics-Informed Fine-Tuning.

Zecheng Zhang, Christian Moya, Lu Lu, Guang Lin, Hayden Schaeffer

CoRR, 2024

Neural Scaling Laws of Deep ReLU and Deep Operator Network: A Theoretical Study.

Hao Liu, Zecheng Zhang, Wenjing Liao, Hayden Schaeffer

CoRR, 2024

Time-Series Forecasting, Knowledge Distillation, and Refinement within a Multimodal PDE Foundation Model.

Derek Jollie, Jingmin Sun, Zecheng Zhang, Hayden Schaeffer

CoRR, 2024

PROSE-FD: A Multimodal PDE Foundation Model for Learning Multiple Operators for Forecasting Fluid Dynamics.

Yuxuan Liu, Jingmin Sun, Xinjie He, Griffin Pinney, Zecheng Zhang, Hayden Schaeffer

CoRR, 2024

LeMON: Learning to Learn Multi-Operator Networks.

Jingmin Sun, Zecheng Zhang, Hayden Schaeffer

CoRR, 2024

Deep Analysis of Time Series Data for Smart Grid Startup Strategies: A Transformer-LSTM-PSO Model Approach.

Zecheng Zhang

CoRR, 2024

Enhancing Visual Question Answering through Ranking-Based Hybrid Training and Multimodal Fusion.

Peiyuan Chen, Zecheng Zhang, Yiping Dong, Li Zhou, Han Wang

CoRR, 2024

RelBench: A Benchmark for Deep Learning on Relational Databases.

Joshua Robinson, Rishabh Ranjan, Weihua Hu, Kexin Huang, Jiaqi Han, Alejandro Dobles, Matthias Fey, Jan Eric Lenssen, Yiwen Yuan, Zecheng Zhang, Xinwei He, Jure Leskovec

CoRR, 2024

CRAB: Cross-environment Agent Benchmark for Multimodal Language Model Agents.

Tianqi Xu, Linyao Chen, Dai-Jie Wu, Yanjun Chen, Zecheng Zhang, Xiang Yao, Zhiqiang Xie, Yongchao Chen, Shilong Liu, Bochen Qian, Philip Torr, Bernard Ghanem, Guohao Li

CoRR, 2024

Image anomaly detection and prediction scheme based on SSA optimized ResNet50-BiGRU model.

Qianhui Wan, Zecheng Zhang, Liheng Jiang, Zhaoqi Wang, Yan Zhou

CoRR, 2024

Theoretical Analysis of Meta Reinforcement Learning: Generalization Bounds and Convergence Guarantees.

Cangqing Wang, Mingxiu Sui, Dan Sun, Zecheng Zhang, Yan Zhou

CoRR, 2024

Automatic News Generation and Fact-Checking System Based on Language Processing.

Xirui Peng, Qiming Xu, Zheng Feng, Haopeng Zhao, Lianghao Tan, Yan Zhou, Zecheng Zhang, Chenwei Gong, Yingqiao Zheng

CoRR, 2024

Towards a Foundation Model for Partial Differential Equations: Multi-Operator Learning and Extrapolation.

Jingmin Sun, Yuxuan Liu, Zecheng Zhang, Hayden Schaeffer

CoRR, 2024

MODNO: Multi Operator Learning With Distributed Neural Operators.

Zecheng Zhang

CoRR, 2024

PyTorch Frame: A Modular Framework for Multi-Modal Tabular Learning.

Weihua Hu, Yiwen Yuan, Zecheng Zhang, Akihiro Nitta, Kaidi Cao, Vid Kocijan, Jure Leskovec, Matthias Fey

CoRR, 2024

Conformalized-DeepONet: A Distribution-Free Framework for Uncertainty Quantification in Deep Operator Networks.

Christian Moya, Amirhossein Mollaali, Zecheng Zhang, Lu Lu, Guang Lin

CoRR, 2024

Projection of future drought impacts on millet yield in northern Shanxi of China using ensemble machine learning approach.

Shiwei Zhou, Yangzhong Wu, Chu Wang, Huayu Lu, Zecheng Zhang, Zijin Liu, Yongdeng Lei, Fu Chen

Comput. Electron. Agric., 2024

Solving Inverse Problems with Latent Diffusion Models via Hard Data Consistency.

Bowen Song, Soo Min Kwon, Zecheng Zhang, Xinyu Hu, Qing Qu, Liyue Shen

Proceedings of the Twelfth International Conference on Learning Representations, 2024

2023

Theoretical perspective on synthetic man-made life: Learning from the origin of life.

Lu Peng, Zecheng Zhang, Xianyi Wang, Weiyi Qiu, Liqian Zhou, Hui Xiao, Chunxiuzi Liu, Shaohua Tang, Zhiwei Qin, Jiakun Jiang, Zengru Di, Yu Liu

Quant. Biol., December, 2023

Multi-agent Reinforcement Learning Aided Sampling Algorithms for a Class of Multiscale Inverse Problems.

Eric T. Chung, Wing Tat Leung, Sai-Mang Pun, Zecheng Zhang

J. Sci. Comput., August, 2023

Hybrid explicit-implicit learning for multiscale problems with time dependent source.

Yalchin Efendiev, Wing Tat Leung, Wenyuan Li, Zecheng Zhang

Commun. Nonlinear Sci. Numer. Simul., June, 2023

B-DeepONet: An enhanced Bayesian DeepONet for solving noisy parametric PDEs using accelerated replica exchange SGLD.

Guang Lin, Christian Moya, Zecheng Zhang

J. Comput. Phys., 2023

Learning the dynamical response of nonlinear non-autonomous dynamical systems with deep operator neural networks.

Guang Lin, Christian Moya, Zecheng Zhang

Eng. Appl. Artif. Intell., 2023

D2NO: Efficient Handling of Heterogeneous Input Function Spaces with Distributed Deep Neural Operators.

Zecheng Zhang, Christian Moya, Lu Lu, Guang Lin, Hayden Schaeffer

CoRR, 2023

Restoring the Discontinuous Heat Equation Source Using Sparse Boundary Data and Dynamic Sensors.

Guang Lin, Na Ou, Zecheng Zhang, Zhidong Zhang

CoRR, 2023

PROSE: Predicting Operators and Symbolic Expressions using Multimodal Transformers.

Yuxuan Liu, Zecheng Zhang, Hayden Schaeffer

CoRR, 2023

Fast Replica Exchange Stochastic Gradient Langevin Dynamics.

Guanxun Li, Guang Lin, Zecheng Zhang, Quan Zhou

CoRR, 2023

IFA-Net: Isomerous Feature-aware Network for Single-view 3D Reconstruction.

Zecheng Zhang, Xianfeng Han, Guoqiang Xiao

Proceedings of the International Joint Conference on Neural Networks, 2023

2022

Distributed-memory tensor completion for generalized loss functions in python using new sparse tensor kernels.

Navjot Singh, Zecheng Zhang, Xiaoxiao Wu, Naijing Zhang, Siyuan Zhang, Edgar Solomonik

J. Parallel Distributed Comput., 2022

Multi-variance replica exchange SGMCMC for inverse and forward problems via Bayesian PINN.

Guang Lin, Yating Wang, Zecheng Zhang

J. Comput. Phys., 2022

NH-PINN: Neural homogenization-based physics-informed neural network for multiscale problems.

Wing Tat Leung, Guang Lin, Zecheng Zhang

J. Comput. Phys., 2022

Efficient hybrid explicit-implicit learning for multiscale problems.

Yalchin Efendiev, Wing Tat Leung, Guang Lin, Zecheng Zhang

J. Comput. Phys., 2022

BelNet: Basis enhanced learning, a mesh-free neural operator.

Zecheng Zhang, Wing Tat Leung, Hayden Schaeffer

CoRR, 2022

A replica exchange preconditioned Crank-Nicolson Langevin dynamic MCMC method for Bayesian inverse problems.

Na Ou, Zecheng Zhang, Guang Lin

CoRR, 2022

On Learning the Dynamical Response of Nonlinear Control Systems with Deep Operator Networks.

Guang Li, Christian Moya, Zecheng Zhang

CoRR, 2022

Computational multiscale method for parabolic wave approximations in heterogeneous media.

Eric T. Chung, Yalchin Efendiev, Sai-Mang Pun, Zecheng Zhang

Appl. Math. Comput., 2022

SAIS: Supervising and Augmenting Intermediate Steps for Document-Level Relation Extraction.

Yuxin Xiao, Zecheng Zhang, Yuning Mao, Carl Yang, Jiawei Han

Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2022

2021

Computational multiscale methods for quasi-gas dynamic equations.

Boris Chetverushkin, Eric T. Chung, Yalchin Efendiev, Sai-Mang Pun, Zecheng Zhang

J. Comput. Phys., 2021

A multi-stage deep learning based algorithm for multiscale model reduction.

Eric T. Chung, Wing Tat Leung, Sai-Mang Pun, Zecheng Zhang

J. Comput. Appl. Math., 2021

Accelerated replica exchange stochastic gradient Langevin diffusion enhanced Bayesian DeepONet for solving noisy parametric PDEs.

Guang Lin, Christian Moya, Zecheng Zhang

CoRR, 2021

Theoretical and numerical studies of inverse source problem for the linear parabolic equation with sparse boundary measurements.

Guang Lin, Zecheng Zhang, Zhidong Zhang

CoRR, 2021

HEI: hybrid explicit-implicit learning for multiscale problems.

Yalchin Efendiev, Wing Tat Leung, Guang Lin, Zecheng Zhang

CoRR, 2021

Multi-variance replica exchange stochastic gradient MCMC for inverse and forward Bayesian physics-informed neural network.

Guang Lin, Yating Wang, Zecheng Zhang

CoRR, 2021

Computational multiscale methods for parabolic wave approximations in heterogeneous media.

Eric T. Chung, Yalchin Efendiev, Sai-Mang Pun, Zecheng Zhang

CoRR, 2021

A deep neural network approach on solving the linear transport model under diffusive scaling.

Liu Liu, Tieyong Zeng, Zecheng Zhang

CoRR, 2021

2020

Multi-agent Reinforcement Learning Accelerated MCMC on Multiscale Inversion Problem.

Eric T. Chung, Yalchin Efendiev, Wing Tat Leung, Sai-Mang Pun, Zecheng Zhang

CoRR, 2020

A multi-stage deep learning based algorithm for multiscale modelreduction.

Eric T. Chung, Wing Tat Leung, Sai-Mang Pun, Zecheng Zhang

CoRR, 2020

Learning Algorithms for Coarsening Uncertainty Space and Applications to Multiscale Simulations.

Zecheng Zhang, Eric T. Chung, Yalchin Efendiev, Wing Tat Leung

CoRR, 2020

paper2repo: GitHub Repository Recommendation for Academic Papers.

Huajie Shao, Dachun Sun, Jiahao Wu, Zecheng Zhang, Aston Zhang, Shuochao Yao, Shengzhong Liu, Tianshi Wang, Chao Zhang, Tarek F. Abdelzaher

Proceedings of the WWW '20: The Web Conference 2020, Taipei, Taiwan, April 20-24, 2020, 2020

2019

Enabling Distributed-Memory Tensor Completion in Python using New Sparse Tensor Kernels.

Zecheng Zhang, Xiaoxiao Wu, Naijing Zhang, Siyuan Zhang, Edgar Solomonik

CoRR, 2019

Non-local Attention Learning on Large Heterogeneous Information Networks.

Yuxin Xiao, Zecheng Zhang, Carl Yang, Chengxiang Zhai

Proceedings of the 2019 IEEE International Conference on Big Data (IEEE BigData), 2019