Italiano(IT) Compute-optimal data scaling for neural surrogates via multi-fidelity training

新方法使用多保真度数据优化偏微分方程的神经代理模型训练

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-15 04:00

一篇新研究论文介绍了一种更有效地训练偏微分方程（PDE）神经代理模型的方法。该方法利用了 PDE 模拟的可调成本，通过在不同保真度数据的混合物上进行训练来实现。这种多保真度训练可以实现针对数据生成计算的帕累托最优误差缩放，以少至 6.3 倍的数据生成计算量达到与仅使用高保真度训练相当的精度。研究结果在六个 PDE 问题、各种误差源和四种代理模型架构上得到了验证。 AI

影响优化了训练 AI 模型的生成数据计算量，可能降低科学计算领域的研究成本并加速研究。

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arXiv cs.AI TIER_1 Italiano(IT) · Paul Setinek, Fabian Paischer, Nils Thuerey, Johannes Brandstetter, Felix Koehler · 2026-10-15 04:00

通过多保真度训练实现神经代理模型的可计算最优数据缩放

arXiv:2610.17221v1 Announce Type: cross Abstract: Neural surrogates for Partial Differential Equations (PDEs) are usually trained on synthetic data from numerical simulators. As models grow and scaling laws demand larger datasets, the cost of data generation often outweighs the t…

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通过多保真度训练实现神经代理模型的可计算最优数据缩放

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