Hypoelliptic and Non-Markovian stochastic dynamical systems in machine learning and mathematical finance: from theory to application

机器学习和数学金融中的亚椭圆和非马尔可夫随机动力系统：从理论到应用

• 批准号: 2420029
• 负责人: Qi Feng
• 金额: $ 16.52万
• 依托单位: Florida State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-02-01 至 2026-05-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2420029&HistoricalAwards=false
• 关键词: Hypoelliptic; Non; Markovian; stochastic; dynamical

This project investigates stochastic analysis and numerical algorithms for stochastic dynamical systems, together with their applications in machine learning and finance. The first part focuses on the foundations of machine learning/data science, which guarantees the theoretical convergence of numerical algorithms (e.g., stochastic gradient descent, Markov Chain Monte Carlo) in non-convex optimization and multi-modal distribution sampling. This project will develop algorithms to solve such problems in big data and engineering, which include uncertainty quantification in AI safety problems, control robotics motions, and image processing. The second part focuses on the stochastic models in mathematical finance and algorithm designs in option/asset pricing. The applications in this part target efficient algorithms for path-dependent option pricing with rough volatilities, which are expected to significantly impact some computation-oriented financial instruments, such as model-based algorithm trading involving rough volatility and high-frequency data. This project will provide support and research opportunities for graduate and undergraduate students. The stochastic systems in this project possess degenerate, mean-field, or non-Markovian properties. In the first part, the PI will study the "hypocoercivity" (i.e., convergence to equilibrium) for highly degenerate and mean-field stochastic dynamical systems and their applications to algorithms design in machine learning. One of the proposed topics will focus on the (non)-asymptotic analysis of the general degenerate/mean-field system and its exponential convergence rate to the equilibrium (e.g., Vlasov-Fokker-Planck equations; Langevin dynamics on higher order nilpotent Lie groups). As applications of the convergence of such dynamics, the PI will design algorithms focusing on non-convex optimizations and distribution samplings in machine learning. In the second part, the PI will study non-Markovian stochastic dynamical systems capturing path-dependent and mean-field features of the financial market. The topics include path-dependent PDEs, stochastic Volterra integral equations, conditional mean-field SDEs, and the Volterra signatures. The PI focuses on addressing the fundamental issues, including the density for the rough volatility model and conditional mean-field SDEs and the structure of Volterra signatures. Furthermore, the PI focuses on designing efficient numerical algorithms using the Volterra signature and deep neural networks. These algorithms target solving path-dependent PDEs, path-dependent option pricing, and optimal stopping/switching problems.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该项目研究随机动力系统的随机分析和数值算法，以及它们在机器学习和金融中的应用。第一部分侧重于机器学习/数据科学的基础，它保证了数值算法的理论收敛性（例如，随机梯度下降，马尔可夫链蒙特卡罗）在非凸优化和多峰分布抽样。该项目将开发算法来解决大数据和工程中的此类问题，其中包括人工智能安全问题中的不确定性量化，控制机器人运动和图像处理。第二部分主要介绍数理金融学中的随机模型和期权/资产定价的算法设计。这部分的应用目标是粗糙波动率下的路径依赖期权定价的有效算法，预计将对一些面向计算的金融工具产生重大影响，例如基于模型的算法交易，涉及粗糙波动率和高频数据。该项目将为研究生和本科生提供支持和研究机会。在这个项目中的随机系统具有退化，平均场，或非马尔可夫性质。在第一部分中，PI将研究“低血糖”（即，收敛到平衡）的高度退化和平均场随机动力系统及其在机器学习算法设计中的应用。其中一个建议的主题将集中在一般退化/平均场系统的（非）渐近分析及其指数收敛到平衡点的速度（例如，Vlasov-Fokker-Planck方程;高阶幂零李群上的朗之万动力学）。作为这种动态收敛的应用，PI将设计专注于机器学习中的非凸优化和分布采样的算法。在第二部分中，PI将研究非马尔可夫随机动力系统捕获路径依赖和平均场特征的金融市场。这些主题包括路径相关偏微分方程，随机沃尔泰拉积分方程，条件平均场随机微分方程，和沃尔泰拉签名。PI侧重于解决基本问题，包括粗糙波动率模型和条件平均场随机微分方程的密度和沃尔泰拉签名的结构。此外，PI专注于使用沃尔泰拉签名和深度神经网络设计高效的数值算法。这些算法的目标是解决路径依赖偏微分方程、路径依赖期权定价和最优停止/切换问题。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。