25 citations to https://www.mathnet.ru/rus/danma452
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Yusuf Falola, Siddharth Misra, Andres Nunez, “Leveraging transfer learning for reliable CO2 storage forecasting across diverse operational conditions”, Fuel, 403 (2026), 135990
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Vincent C. Scholz, Yaohua Zang, Phaedon-Stelios Koutsourelakis, “Weak neural variational inference for solving Bayesian inverse problems without forward models: Applications in elastography”, Computer Methods in Applied Mechanics and Engineering, 433 (2025), 117493
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Abhishek Chandra, Bram Daniels, Mitrofan Curti, Koen Tiels, Elena A. Lomonova, “Magnetic Hysteresis Modeling With Neural Operators”, IEEE Trans. Magn., 61:1 (2025), 1
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Karn Tiwari, N. M. Anoop Krishnan, Prathosh A. P., “CoNO: Complex neural operator for continous dynamical physical systems”, APL Machine Learning, 3:2 (2025)
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Jae Yong Lee, Seungchan Ko, Youngjoon Hong, “Finite Element Operator Network for Solving Elliptic-Type Parametric PDEs”, SIAM J. Sci. Comput., 47:2 (2025), C501
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Shailesh Garg, Souvik Chakraborty, “Distribution free uncertainty quantification for neuroscience-inspired deep neural operators”, Journal of Computational Physics, 2025, 114012
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Jingjian Chen, Jie Nie, Ning Song, Min Ye, Zhiqiang Wei, “NS-FUO: Fourier U-type operator based on nested structure”, Appl Intell, 55:7 (2025)
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Zhongshu Xu, Yuan Chen, Dongbin Xiu, “CHEBYSHEV FEATURE NEURAL NETWORK FOR ACCURATE FUNCTION APPROXIMATION”, J Mach Learn Model Comput, 6:2 (2025), 29
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Hong-Kyun Noh, Chaeyun Won, Jongmok Lee, Sanghun Choi, Seungchul Lee, Jae Hyuk Lim, “Physics-informed machine learning: technological trends in turbulence flow, topology optimization, and convergence enhancement with temporal causality consideration”, JMST Adv., 2025
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Tian-ai Zhang, Shi Jin, “AP-MIONet: Asymptotic-Preserving Multiple-Input Neural Operators for Capturing the High-Field Limits of Collisional Kinetic Equations”, J Sci Comput, 104:1 (2025)