Publication

Peer reviewed publications

• Birrell, J., Katsoulakis, M., Rey-Bellet, L., Zhang, B., and Zhu, W., "Nonlinear denoising score matching for enhanced learning of structured distributions." Computer Methods in Applied Mechanics and Engineering. (2025) [link]

• Chen, Z., Katsoulakis, M., Rey-Bellet, L., and Zhu, W., "Statistical guarantees of group-invariant GANs," SIAM/ASA Journal on Uncertainty Quantification. (2025) [link]

• Negrini, E., Gao, A.J., Bowering, A., Zhu, W., and Capogna, L., "Neural Networks for Threshold Dynamics Reconstruction." Inverse Problems and Imaging. (2025) [link]

• Gao, Z., Zhang, Y., Harrington, L., Murdock, C., Martin, E., Manbeck-Mosig, D., Vetrone, S., Tremblay, N., Barker, C., Clark, J., He, L., and Zhu, W., "Determining Mosquito Age Using Surface-enhanced Raman Spectroscopy and Artificial Neural Networks: Insights into the Influence of Origin and Sex." Parasites and Vectors. (2025) [link]

• Chen, S., Kevrekidis, P.G., Zhang, H., and Zhu, W., "Data-Driven Discovery of Conservation Laws from Trajectories via Neural Deflation," Communications in Nonlinear Science and Numerical Simulation. (2024) [link]

• Yang, S., Chen, S., Zhu, W., and Kevrekidis, P.G., "Identification of moment equations via data-driven approaches in nonlinear Schrodinger models," Frontiers in Photonics. (2024)  [link]

• Chen, Z. and Zhu, W., "On the implicit bias of linear equivariant steerable networks," Neural Information Processing Systems (NeurIPS). (2023) [link]

• Chen, Z., Katsoulakis, M., Rey-Bellet, L., and Zhu, W., "Sample complexity of probability divergences under group symmetry," International Conference on Machine Learning (ICML). (2023) [link]

• Zhu, W., Zhang, H., and Kevrekidis, P.G., "Machine learning of independent conservation laws through neural deflation," Physical Review E. (2023) [link]

• Saqlain, S., Zhu, W., Charalampidis, E.G., and Kevrekidis, P.G., "Discovering governing equations in discrete systems using PINNs," Communications in Nonlinear Science and Numerical Simulation. (2023) [link]

• Gao, Z., Harrington, L., Zhu, W., Barrientos, L., Alfonso-Parra, C., Avila, F., Clark, J., and He, L., "Accurate age-grading of field-collected mosquitoes reared under ambient conditions using surface-enhanced Raman spectroscopy and artificial neural networks," Journal of Medical Entomology. (2023) [link]

• Birrell, J., Katsoulakis, M., Rey-Bellet, L., and Zhu. W., "Structure-preserving GANs," International Conference on Machine Learning (ICML). (2022) [link]

• Gao, L., Lin, G., and Zhu. W., "Deformation robust roto-scale-translation equivariant CNNs," Transactions on Machine Learning Research (TMLR), (2022) [link]

• Zhu, W., Khademi, W., Charalampidis, E.G., and Kevrekidis, P.G., "Neural networks enforcing physical symmetries in nonlinear dynamical lattices: the case example of the Ablowitz-Ladik model," Physica D: Nonlinear Phenomena, (2022) [link]

• Zhu, W., Qiu, Q., Calderbank, R., Sapiro, G., and Cheng, X., "Scaling-translation-equivariant networks with decomposed convolutional filters," Journal of Machine Learning Research (JMLR), (2022) [link]

• Wang, B., Lin, A.T., Shi, Z., Zhu, W., Yin, P., Bertozzi, A., and Osher, S., "Adversarial defense via data dependent activation function and total variation minimization," Inverse Problems and Imaging, (2020) [link]

• Zhu, W., Shi, Z., and Osher, S., "Low dimensional manifold model in hyperspectral image reconstruction," Advances in Computer Vision and Pattern Recognition. Springer, Cham. (2020) [link]

• Zhu, W., Qiu, Q., Wang, B., Lu, J., Sapiro, G., and Daubechies, I., "Stop memorizing: a data-dependent regularization framework for intrinsic pattern learning," SIAM Journal on Mathematics of Data Science, (2019) [link]

• Wu, Z., Zhu, W., Chanussot, J., Xu, Y., and Osher, S., "Hyperspectral anomaly detection via global and local joint modeling of background," IEEE Transactions on Signal Processing, (2019) [link]

• Wang, B., Luo, X., Li, Z., Zhu, W., Shi, Z., and Osher, S., "Deep neural nets with interpolating function as output activation," 32nd Conference on Neural Information Processing Systems (NeurIPS), (2018) [link]

• Zhu, W.*, Shi, Z.*, and Osher, S., "Scalable low dimensional manifold model in the reconstruction of noisy and incomplete hyperspectral images," IEEE Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), (2018) [link]

• Zhu, W., Qiu, Q., Huang, J., Calderbank, R., Sapiro, G., and Daubechies, I., "LDMNet: Low dimensional manifold regularized neural networks," IEEE Conference on Computer Vision and Pattern Recognition (CVPR), (2018) [link]

• Zhu, W., Wang, B., Barnard, R., Hauck, C., Jenko, F., and Osher, S., "Scientific data interpolation with low dimensional manifold model," Journal of Computational Physics, (2018) [link]

• Shi, Z., Osher, S., and Zhu, W., "Generalization of the weighted nonlocal laplacian in low dimensional manifold model," Journal of Scientific Computing, (2018) [link]

• Zhu, W., Chayes, V., Tiard, A., Sanchez, S., Dahlberg, D., Bertozzi, A., Osher, S., Zosso, D., and Kuang, D., "Unsupervised classification in hyperspectral imagery with nonlocal total variation and primal-dual hybrid gradient algorithm," IEEE Transactions on Geoscience and Remote Sensing, (2017) [link]

• Shi, Z., Osher, S., and Zhu, W., "Weighted nonlocal laplacian on interpolation from sparse data," Journal of Scientific Computing, (2017) [link]

• Chayes, V., Miller, K., Bhalerao, R., Luo, J., Zhu, W., Bertozzi, A., Liao, W., and Osher, S., "Pre-processing and classification of hyperspectral imagery via selective inpainting presentation," IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), (2017) [link]

• Osher, S., Shi, Z., and Zhu, W., "Low dimensional manifold model for image processing," SIAM Journal on Imaging Sciences, (2017) [link]

Preprints

• Adriazola, J., Zhu, W., Kevrekidis, P.G, and Aceves, A., "Computer Assisted Discovery of Integrability via SILO: Sparse Identification of Lax Operators." (2025) [link]

• Chen, Z., Gu, H., Katsoulakis, M., Rey-Bellet, L., and Zhu, W., "Learning heavy-tailed distributions with Wasserstein-proximal-regularized -divergences." (2024) [link]

• Zhu, W. and Daubechies, I., "Constructing curvelet-like bases and low-redundancy frames." (2019) [link]