在这项工作中，我们提出了一种基于深度学习的新方案，用于解决高维非线性后向随机微分方程（BSDES）。这个想法是将问题重新重新制定为包括本地损失功能的全球优化。本质上，我们使用深神网络及其具有自动分化的梯度近似BSDE的未知解。通过在每个时间步骤定义的二次局部损耗函数中最小化近似值来执行近似值，该局部损失函数始终包括终端条件。这种损失函数是通过用终端条件迭代时间积分的Euler离散化来获得的。我们的公式可以促使随机梯度下降算法不仅要考虑到每个时间层的准确性，而且会收敛到良好的局部最小值。为了证明我们的算法的性能，提供了几种高维非线性BSDE，包括金融中的定价问题。

This paper presents a conversational AI platform called Flowstorm. Flowstorm is an open-source SaaS project suitable for creating, running, and analyzing conversational applications. Thanks to the fast and fully automated build process, the dialogues created within the platform can be executed in seconds. Furthermore, we propose a novel dialogue architecture that uses a combination of tree structures with generative models. The tree structures are also used for training NLU models suitable for specific dialogue scenarios. However, the generative models are globally used across applications and extend the functionality of the dialogue trees. Moreover, the platform functionality benefits from out-of-the-box components, such as the one responsible for extracting data from utterances or working with crawled data. Additionally, it can be extended using a custom code directly in the platform. One of the essential features of the platform is the possibility to reuse the created assets across applications. There is a library of prepared assets where each developer can contribute. All of the features are available through a user-friendly visual editor.