English(EN) TORNADO: Adaptive Latent Stochastic Transport for Calibrated Probabilistic PDE Forecasting

TORNADO 框架通过自适应随机性增强 PDE 预测

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

研究人员推出 TORNADO，一个用于偏微分方程 (PDE) 概率预测的新框架。该方法将潜在随机性适应于局部动力学，与使用固定全局随机性的现有方法相比，提高了不确定性校准。TORNADO 将预测表述为 VAE 编码器后验之间的随机传输，并使用解析条件端点目标进行训练，附加了一个能量距离正则化项以实现更好的分布对齐。 AI

影响提高了 PDE 预测中的不确定性量化，可能导致科学研究中更可靠的模拟。

排序理由该集群包含一篇详细介绍新预测计算框架的学术论文。[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 →

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报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Jorge Mifsut-Benet, Armand Kassa\"i Koupa\"i, Ramon Daniel Regueiro-Espino, Nicolas Baskiotis, Patrick Gallinari · 2026-11-11 05:00

TORNADO：用于校准概率性PDE预测的自适应潜在随机传输

arXiv:2611.10251v2 Announce Type: replace Abstract: Probabilistic neural surrogates for PDEs enable ensemble forecasting, but often exhibit miscalibration when used for uncertainty quantification, especially in compute-constrained, few-step inference regimes. Existing generative …

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报道来源 [1]

TORNADO：用于校准概率性PDE预测的自适应潜在随机传输

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