新的深度算子BSDE方法使用神经网络逼近解

作者 PulseAugur 编辑部 · [1 个来源] · 2026-07-09 04:00

研究人员开发了一种名为深度算子BSDE的新数值方法，用于逼近倒向随机微分方程（BSDE）的解算子。该方法利用维纳混沌分解和经典的欧拉格式，在最少假设下证明了收敛性，并在特定场景下提供了收敛速率。该方法已通过神经网络实现，并通过各种展示其准确性的数值示例进行了验证。 AI

影响该方法可以提高复杂金融建模和风险评估的准确性和效率。

排序理由该集群包含一篇详细介绍求解数学方程新数值方法的学术论文。[lever_c_demoted from research: ic=1 ai=0.7]

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arXiv cs.LG TIER_1 English(EN) · Pere Diaz-Lozano, Giulia Di Nunno · 2026-07-09 04:00

Deep Operator BSDE：一种逼近解算子数值方法

arXiv:2412.03405v3 Announce Type: replace-cross Abstract: Motivated by dynamic risk measures and conditional $g$-expectations, in this work we propose a numerical method to approximate the solution operator given by a Backward Stochastic Differential Equation (BSDE). The main ing…