Low-Complexity Fiber Nonlinearity Compensation Based on Operator Learning Over 12 075 km Single Model Fiber

Tekijät: He, X., Yan, L., Jiang, L., Yi, A., Yu, Y., Pu, Z., Pan, W., Luo, B.

Lähde: Journal of Lightwave Technology J. Lightwave Technol. Lightwave Technology, Journal of. 42(23):8117-8126 Dec, 2024

Predicting nonequilibrium Green’s function dynamics and photoemission spectra via nonlinear integral operator learning

Tekijät: Zhu, Yuanran, Yin, Jia, Reeves, Cian C, Yang, Chao, Vlček, Vojtěch

Lähde: Machine Learning: Science and Technology. 6(1)

Asiasanat: Information and Computing Sciences, Applied Computing, Machine Learning, Machine Learning and Artificial Intelligence, operator-learning, nonequilibrium quantum many-body theory, nonlinear integro-differential equation, dynamics reduction, Applied computing, Machine learning

Tiedoston kuvaus: application/pdf

Linkki aineistoon: https://escholarship.org/uc/item/6wx407j1
https://escholarship.org/content/qt6wx407j1/qt6wx407j1.pdf

Learning Transition Operators From Sparse Space-Time Samples

Tekijät: Kummerle, C., Maggioni, M., Tang, S.

Lähde: IEEE Transactions on Information Theory IEEE Trans. Inform. Theory Information Theory, IEEE Transactions on. 70(9):6412-6446 Sep, 2024

A Deep Transfer Operator Learning Method for Temperature Field Reconstruction in a Lithium-Ion Battery Pack

Tekijät: Wang, Y., Xiong, C., Ju, C., Yang, G., Chen, Y., Yu, X.

Lähde: IEEE Transactions on Industrial Informatics IEEE Trans. Ind. Inf. Industrial Informatics, IEEE Transactions on. 20(6):8089-8101 Jun, 2024

Deep Multiphysics Fields Solver Established on Operator Learning Transformer and Finite Element Method

Tekijät: Wang, Y.

Lähde: IEEE Journal on Multiscale and Multiphysics Computational Techniques IEEE J. Multiscale Multiphys. Comput. Tech. Multiscale and Multiphysics Computational Techniques, IEEE Journal on. 9:341-352 2024

Local Monotone Operator Learning Using Non-Monotone Operators: MnM-MOL

Tekijät: John, M., Chand, J.R., Jacob, M.

Lähde: IEEE Transactions on Computational Imaging IEEE Trans. Comput. Imaging Computational Imaging, IEEE Transactions on. 10:742-751 2024

Memory-Efficient Model-Based Deep Learning With Convergence and Robustness Guarantees

Tekijät: Pramanik, A., Zimmerman, M.B., Jacob, M.

Lähde: IEEE Transactions on Computational Imaging IEEE Trans. Comput. Imaging Computational Imaging, IEEE Transactions on. 9:260-275 2023

Gradient-Based Learning of Discrete Structured Measurement Operators for Signal Recovery

Tekijät: Sauder, J., Genzel, M., Jung, P.

Lähde: IEEE Journal on Selected Areas in Information Theory IEEE J. Sel. Areas Inf. Theory Selected Areas in Information Theory, IEEE Journal on. 3(3):481-492 Sep, 2022

In-context operator learning with data prompts for differential equation problems.

Tekijät: Yang, Liu, Liu, Siting, Osher, Stanley, Meng, Tingwei

Lähde: Proceedings of the National Academy of Sciences of the United States of America. 120(39)

Asiasanat: artificial intelligence, differential equation, in-context learning, meta-learning, operator learning

Tiedoston kuvaus: application/pdf

Linkki aineistoon: https://escholarship.org/uc/item/9b80d5p1
https://escholarship.org/content/qt9b80d5p1/qt9b80d5p1.pdf

U-DeepONet: U-Net enhanced deep operator network for geologic carbon sequestration

Tekijät: Waleed Diab, Mohammed Al Kobaisi

Lähde: Scientific Reports, Vol 14, Iss 1, Pp 1-12 (2024)

Asiasanat: Scientific machine learning, Neural operator learning, DeepONet, CO2 Sequestration, Flow and transport in porous media, U-Net, Medicine, Science

Tiedoston kuvaus: electronic resource

Relation: https://doaj.org/toc/2045-2322

Linkki aineistoon: https://doaj.org/article/5b4ebe4bf3be492aadf2bbd3b525d581

MODNO: Multi-Operator learning with Distributed Neural Operators

Tekijät: Zhang, Zecheng

Lähde: In Computer Methods in Applied Mechanics and Engineering 1 November 2024 431

Dynamic Gaussian Graph Operator: Learning parametric partial differential equations in arbitrary discrete mechanics problems

Tekijät: Wang, Chu, Wu, Jinhong, Wang, Yanzhi, Zha, Zhijian, Zhou, Qi ^⁎

Lähde: In Engineering Applications of Artificial Intelligence 15 May 2025 148

Phase-field hydraulic fracturing operator network based on En-DeepONet with integrated physics-informed mechanisms

Tekijät: Wang, Xiaoqiang ^a, Li, Peichao ^b, Lu, Detang ^{a, ⁎}

Lähde: In Computer Methods in Applied Mechanics and Engineering 15 March 2025 437

Approximation of solution operators for high-dimensional PDEs

Tekijät: Gaby, Nathan, Ye, Xiaojing ^⁎

Lähde: In Journal of Computational Physics 1 March 2025 524

Learning the Proximity Operator in Unfolded ADMM for Phase Retrieval

Tekijät: Vial, P., Magron, P., Oberlin, T., Fevotte, C.

Lähde: IEEE Signal Processing Letters IEEE Signal Process. Lett. Signal Processing Letters, IEEE. 29:1619-1623 2022

Fine-tune language models as multi-modal differential equation solvers

Tekijät: Yang, Liu, Liu, Siting, Osher, Stanley J. ^⁎

Lähde: In Neural Networks August 2025 188

Distribution free uncertainty quantification for neuroscience-inspired deep neural operators

Tekijät: Garg, Shailesh ^{a, ⁎}, Chakraborty, Souvik ^{a, b, c, ⁎}

Lähde: In Journal of Computational Physics 1 August 2025 534

A hybrid kernel-free boundary integral method with operator learning for solving parametric partial differential equations in irregular domains

Tekijät: Ling, Shuo ^{a, 1}, Tan, Liwei ^{a, 1}, Ying, Wenjun ^{b, ⁎}

Lähde: In Communications in Nonlinear Science and Numerical Simulation August 2025 147

Dilated convolution neural operator for multiscale partial differential equations

Tekijät: Xu, Bo ^a, Liu, Xinliang ^{b, c, ⁎}, Zhang, Lei ^d

Lähde: In Journal of Computational and Applied Mathematics June 2025 461

Uncertainty quantification for DeepONets with ensemble Kalman inversion

Tekijät: Pensoneault, Andrew, Zhu, Xueyu ^⁎

Lähde: In Journal of Computational Physics 15 February 2025 523