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English(EN) The importance of tensor handling and implementation of physics-informed DeepONets

新的PI-DeepONet架构大幅降低了高维问题的训练成本

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了一种改进的物理信息深度算子网络（PI-DeepONets）架构，该架构显著降低了高维问题的训练成本。这种新的表述在数学上等同于原始表述，它优化了张量处理并减少了分支网络中的冗余计算。所提出的PI-DeepONet架构在达到与原始架构相当的准确性的同时，在2D实验中展示了高达3.75倍的速度提升，并使得在NVIDIA A100 GPU等硬件上进行训练成为可能，而这些硬件之前受到内存限制。 AI

影响 使物理信息学习能够应用于计算成本限制的更复杂、更高维度的问题。

排序理由 研究论文，详细介绍了物理信息神经网络架构的新实现。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

新的PI-DeepONet架构大幅降低了高维问题的训练成本

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研究论文，详细介绍了物理信息神经网络架构的新实现。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Nahum Banks, Gustavo A. M. de Almeida, Sergio Maldonado ·

    张量处理和物理信息深度神经网络实现的重要性

    arXiv:2610.22157v1 Announce Type: new Abstract: Physics-informed neural networks (PINNs) reduce or eliminate the need for large labelled datasets by enforcing governing equations during training. Physics-informed Deep Operator Networks (PI-DeepONets) extend this approach to learn…

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