English(EN) Deep learning based numerical approximation algorithms for stochastic partial differential equations

深度学习算法在高维SPDE问题上提供精确解

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-02 04:00

一篇新研究论文介绍了一种基于深度学习的算法，用于逼近随机偏微分方程（SPDEs）的解。该方法利用神经网络根据噪声过程的实现来估计SPDE解，从而能够计算均值和方差等泛函。该算法在涉及各种SPDEs的模拟中表现出准确性和效率，包括随机热方程和Black-Scholes方程，即使在高达100个空间维度的情况下也是如此。 AI

排序理由该集群包含一篇研究论文，详细介绍了使用深度学习解决复杂数学方程的新颖算法。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv stat.ML TIER_1 English(EN) · Christian Beck, Sebastian Becker, Patrick Cheridito, Arnulf Jentzen, Ariel Neufeld · 2026-09-02 04:00

基于深度学习的随机偏微分方程数值近似算法

arXiv:2012.01194v3 Announce Type: replace-cross Abstract: In this article, we introduce a deep learning based approximation algorithm for SPDEs. Our approach employs neural networks to approximate the solutions of SPDEs along given realizations of the driving noise process. If ap…

本文如何被排名

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基于深度学习的随机偏微分方程数值近似算法

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