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Monte Carlo Neural Operator for Learning PDEs via Probabilistic Representation.

, , , , , , , и . CoRR, (2023)

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Huili Shi

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Monte Carlo Neural Operator for Learning PDEs via Probabilistic Representation., , , , , , , и . CoRR, (2023)NeuralStagger: Accelerating Physics-constrained Neural PDE Solver with Spatial-temporal Decomposition., , , , , , и . ICML, том 202 из Proceedings of Machine Learning Research, стр. 13993-14006. PMLR, (2023)Cooperative Policy Learning with Pre-trained Heterogeneous Observation Representations., , , , , , и . AAMAS, стр. 1191-1199. ACM, (2021)Learning Physics-Informed Neural Networks without Stacked Back-propagation., , , , , , , и . AISTATS, том 206 из Proceedings of Machine Learning Research, стр. 3034-3047. PMLR, (2023)On DE: Its Nature and Features., , , и . CLSW, том 8229 из Lecture Notes in Computer Science, стр. 56-65. Springer, (2013)Enabling RFID-Based Tracking for Multi-Objects with Visual Aids: A Calibration-Free Solution., , , и . INFOCOM, стр. 1281-1290. IEEE, (2020)LordNet: An efficient neural network for learning to solve parametric partial differential equations without simulated data., , , , , , , и . Neural Networks, (2024)Cooperative Policy Learning with Pre-trained Heterogeneous Observation Representations., , , , , , и . CoRR, (2020)