English(EN) WaSP: Wasserstein Data Synthesis Pipeline for Solver-Free Operator Learning

新 WaSP 方法训练 AI 在无需昂贵数据的情况下求解偏微分方程

作者 PulseAugur 编辑部 · [1 个来源] · 2026-11-06 05:00

研究人员开发了 Wasserstein 数据合成管道 (WaSP)，这是一种新颖的训练神经网络算子以求解偏微分方程 (PDE) 的方法，无需昂贵的输入-解对。WaSP 通过合成逐渐缓解分布偏移的训练数据，解决了现有无求解器方法的局限性。该方法在 Allen-Cahn 和 Kuramoto-Sivashinsky 方程等复杂 PDE 上取得了显著改进，解误差最多可降低 9.5 倍。 AI

影响该方法可以显著降低科学模拟的 AI 模型训练的计算成本，从而加速依赖于求解 PDE 的领域的研究。

排序理由该集群包含一篇详细介绍 AI 驱动的科学计算新方法的论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · Ryan Liu, Chuwei Wang, Ricardo Baptista, Andrew Stuart, Anima Anandkumar · 2026-11-06 05:00

WaSP：用于无求解器算子学习的 Wasserstein 数据合成管道

arXiv:2611.07646v1 Announce Type: cross Abstract: Neural operators offer fast surrogates for solving PDEs, but training them typically requires expensive input-solution pairs. Two solver-free alternatives include physics-informed learning and training on manufactured data. Physic…

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报道来源 [1]

WaSP：用于无求解器算子学习的 Wasserstein 数据合成管道

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