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English(EN) SPLICE: Sparse Physics Library Identification and Compositional Expansion

新的SPLICE框架发现并组合用于物理模拟的神经算子

研究人员开发了SPLICE,一个新颖的框架,旨在发现和组合神经算子来模拟物理方程,而无需预先标记的数据。SPLICE通过解决一个凸优化问题,从无标签的混合数据中推断出不同物理机制的贡献。这种方法允许预训练库通过识别新的物理算子来适应和增长,即使没有标签,并且不会忘记之前学到的机制。该框架在包括Navier-Stokes方程在内的各种系统中都显示出有效性,其中发现的算子达到了与监督方法相当的准确性。 AI

影响 通过组合学习到的算子,实现对新物理方程的零样本模拟,可能加速科学发现。

排序理由 该集群包含一篇详细介绍用于科学模拟的新框架的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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新的SPLICE框架发现并组合用于物理模拟的神经算子

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该集群包含一篇详细介绍用于科学模拟的新框架的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Lucas Thil, Jesse Read, Guillaume Doquet, Rim Kaddah ·

    SPLICE:稀疏物理库识别与组合式扩展

    arXiv:2610.17029v1 Announce Type: cross Abstract: Neural operator splitting composes a library of neural operators, one per physical mechanism, into zero-shot simulators of new equations. Existing approaches, however, require these mechanisms and their labels to be specified in a…