Z. Zhang, F. Bao, G. Zhang, Improving the Expressive Power of Deep Neural Networks through Integral Activation Transform, submitted (https://arxiv.org/abs/2312.12578).

F. Bao, Z. Zhang, G. Zhang, A Unified Filter Method for Jointly Estimating State and Parameters of Stochastic Dynamical Systems via the Ensemble Score Filter, submitted (https://arxiv.org/abs/2312.10503). 

Z. Zhang, F. Bao, G. Zhang, An ensemble score filter for tracking high-dimensional nonlinear dynamical system, submitted (https://arxiv.org/pdf/2309.00983). 

M. Yang, P. Wang, D. del-Castillo-Negrete, Y. Cao, G. Zhang, A pseudo-reversible normalizing flow for stochastic dynamical systems with various initial conditions, submitted. (https://arxiv.org/abs/2306.05580)

F. Bao, Z. Zhang, G. Zhang, A score-based nonlinear filter for data assimilation, submitted. (https://arxiv.org/abs/2306.09282).

Z. Zhang, F. Bao, L. Ju, G. Zhang, TransNet: Transferable neural networks for partial differential equations, Journal of Scientific Computing, to appear in 2024.

H. Tran, Q. Du, G. Zhang, Convergence analysis for a nonlocal gradient descent method via directional Gaussian smoothing, submitted. (https://arxiv.org/abs/2302.06404)

Y. Liu, M. Yang, Z. Zhang, F. Bao, Y. Cao, G. Zhang, Diffusion-model-assisted supervised learning of generative models for density estimation, Journal of Machine Learning for Modeling and Computing, 5(1), pp. 25-38, 2024. 

H. Ni, Z. Wu, X. Wu, J. Smith, M. Zachman, J. Zuo, L. Ju, G. Zhang, M. Chi, Quantifying atomically dispersed catalysts using deep learning assisted microscopy analysis, Nano Letters, 23(16), pp. 7442--7448, 2023. 

M. Yang, D. del-Castillo-Negrete, Y. Cao, G. Zhang, A probabilistic scheme for semilinear nonlocal diffusion equations with volume constraints, to appear in SIAM Journal on Numerical Analysis, 2023. (https://arxiv.org/abs/2205.00516)

Q. Ma, X. Bai, E. Feng, G. Zhang, H. Cao, CrysFieldExplorer: rapid optimization of crystal field Hamiltonian, Journal of Applied Crystallography, 56 (4), pp. 1229-1241, 2023. 

J. Smith, Z. Huang, W. Gao, G. Zhang, M. Chi, Atomic resolution cryogenic 4D-STEM imaging robust distortion correction, ACS Nano, Vol 17, pp. 11327-11334, 2023.

Y. Teng, Z. Wang, L. Ju, A. Gruber, G. Zhang, Level set learning with pseudo-reversible neural networks for nonlinear dimension reduction in function approximation, SIAM Journal on Scientific Computing, Vol 45(3), pp. A1148-A1171, 2023.

J. Yin, S. Liu, V. Rashniak, X. Wang, G. Zhang, A scalable transformer model for real-time decision making in neutron scattering experiments, Journal of Machine Learning for Modeling and Computing, 4(1), pp. 95-107, 2023. 

Y. Hao, E. Feng, D. Lu, L. Zimmer, Z. Morgan, B. Chakoumakos, G. Zhang, H. Cao, Machine-learning-assisted automation of single-crystal neutron diffraction, Journal of Applied Crystallography, 56, pp. 519-525, 2023.

J. Yin, G. Zhang, H. Cao, S. Dash, B. Chakoumakos, F. Wang, Toward an autonomous workflow for single crystal neutron diffraction, Communications in Computer and Information Science, Vol. 1690, pp. 244-256, 2022, Springer, Cham.

Chen, Boyuan and Huang, Kuang and Raghupathi, Sunand and Chandratreya, Ishaan and Du, Qiang and Lipson, Hod, Automated discovery of fundamental variables hidden in experimental data. Nature Computational Science 2, 433–442 (2022).

Yuankai Teng, Xiaoping Zhang, Zhu Wang, Lili Ju, Learning Green's Functions of Linear Reaction-Diffusion Equations with Application to Fast Numerical Solver, Proceedings of Third Mathematical and Scientific Machine Learning Conference (MSML 2022), 145 pp. 1–22, 2022. 

H. Tran, D. Lu, and G. Zhang, Exploiting the local parabolic landscapes of adversarial losses to accelerate black-box adversarial attack, Proceedings of 17th European Conference on Computer Vision (ECCV 2022), 2022.

Majdi I. Radaideh, Hoang Tran, Lianshan Lin, Hao Jiang, Drew Winder, Sarma Gorti, Guannan Zhang, Justin Mach, Sarah Cousineau, Model Calibration of the Liquid Mercury Spallation Target using Evolutionary Neural Networks and Sparse Polynomial Expansions, Nuclear Inst. and Methods in Physics Research B, 525(15), pp. 41-54, 2022. 

S. Liu, P. Zhang, D. Lu and G. Zhang, PI3NN: Out-of-distribution-aware prediction intervals from three neural networks, Proceedings of 10th International Conference on Learning Representations (ICLR), 2022.

X. Wu, Z. Wu, Y. Lu, L. Ju, S. Wang, Style Mixing and Patchwise Prototypical Matching for One-Shot Unsupervised Domain Adaptive Semantic Segmentation, Proceedings of Thirty-Sixth AAAI Conference on Artificial Intelligence (AAAI’2022). 

X. Wu, Z. Wu, L. Ju, S. Wang, A One-Stage Domain Adaptation Network with Image Alignment for Unsupervised Nighttime Semantic Segmentation, IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022. (doi: 10.1109/TPAMI.2021.3138829)    

Richard Archibald, Feng Bao, Yanzhao Cao, He Zhang, A backward SDE method for uncertainty quantification in deep learning, Discrete and Continuous Dynamical Systems - S, 2021. (doi: 10.3934/dcdss.2022062)

Richard Archibald, Feng Bao, Kernel learning backward SDE filter for data assimilation, Journal of Computational Physics, Vol. 455, pp. 111009, 2022. 

P. Zhang, S. Liu, D. Lu, R. Sankaran, G. Zhang, An out-of-distribution-aware autoencoder model for reduced chemical kinetics, Discrete & Continuous Dynamical Systems - Series S, 15(4), pp. 913-930, 2022.