Novel Numerical Methods for Nonlinear Stochastic PDEs and High Dimensional Computation

非线性随机偏微分方程和高维计算的新数值方法

• 批准号: 2309626
• 负责人: Xiaobing Feng
• 金额: $ 37.96万
• 依托单位: University of Tennessee Knoxville
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-01 至 2026-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2309626&HistoricalAwards=false
• 关键词: Novel; Numerical; Methods; Nonlinear; Stochastic

Many scientific, engineering, and industrial applications involve random effects. Incorporating uncertainties into mathematical models becomes indispensable in order to develop more accurate and robust mathematical models from biological, engineering, and physical applications, which makes it necessary to consider stochastic partial differential equations as they are the most commonly encountered stochastic models from applications. To seek solutions of those equations require accurate, efficient, and robust computational methods and algorithms, the demand for such methods and algorithms has never been greater. The current approaches for solving stochastic partial differential equations face considerable challenges at large scales, in the meantime, such stochastic models become more complicated as more difficult nonlinearity and noise are considered. The existing numerical approaches are not efficient to solve those problems, which in turn calls for new ideas and approaches. With rapid developments in nontraditional applied sciences such as mathematical finance, image processing, economics, and data science, there is an ever-increasing demand for efficient numerical methods for solving challenging high-dimensional problems such as computing integration and solving partial differential equations. The traditional grid-based methods are hampered by the infamous curse of dimensionality in which the amount of required computations for solving a problem grows exponentially in the dimension. The primary goal of this research project is to develop novel and efficient numerical methods to address those challenges. The project consists of two integral parts. Part I focuses on developing and analyzing efficient numerical methods for solving several nonlinear stochastic partial differential equations which arise from various scientific and engineering applications such as materials science, fluid and quantum mechanics, and optimal control. Part II will be devoted to developing novel numerical methods for high-dimensional computation with a focus on problems of high-dimensional numerical integration and high-dimensional partial differential equations. The overreaching vision of this project is to develop a framework for constructing and analyzing numerical methods for general nonlinear stochastic partial differential equations and to develop new approaches and enabling methods for overcoming the curse of dimensionality challenge for high-dimensional computation. The project will include training of graduate students.This research project develops advanced numerical methods for nonlinear stochastic partial differential equations and high-dimensional computation. Such a timely and advanced project is of great interest to the STEM community as the anticipated numerical methods and algorithms will provide much-needed enabling tools for tackling challenging problems described mathematically by stochastic partial differential equations or involved with high-dimensional computation from many scientific, engineering, and industrial applications as well as AI, machine learning, and data science. This research project is also expected to have a lasting impact on the advancement of numerical stochastic partial differential equations and high-dimensional computation. Moreover, the project will provide a valuable opportunity and resource to train Ph.D. graduate students and to help them to develop necessary applied and computational mathematics as well as AI, machine learning, and data science knowledge and skills so that they can pursue successful careers in either academia or industry in the near future.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.

许多科学、工程和工业应用都涉及随机效应。为了从生物、工程和物理应用中开发更准确和鲁棒的数学模型，将不确定性纳入数学模型变得必不可少，这使得有必要考虑随机偏微分方程，因为它们是应用中最常见的随机模型。为了寻求这些方程的解，需要准确、高效、鲁棒的计算方法和算法，对这种方法和算法的需求从未如此之大。当前求解随机偏微分方程的方法在大尺度上面临着相当大的挑战，同时，随着考虑更困难的非线性和噪声，这种随机模型变得更加复杂。现有的数值方法不能有效地解决这些问题，这反过来又需要新的思路和方法。随着数学金融、图像处理、经济学和数据科学等非传统应用科学的快速发展，对解决计算积分和求解偏微分方程等具有挑战性的高维问题的高效数值方法的需求不断增加。传统的基于网格的方法受到臭名昭著的维度灾难的阻碍，其中解决问题所需的计算量在维度上呈指数级增长。该研究项目的主要目标是开发新颖且有效的数值方法来应对这些挑战。该项目由两个组成部分组成。第一部分重点开发和分析有效的数值方法，用于求解多个非线性随机偏微分方程，这些方程产生于材料科学、流体和量子力学以及最优控制等各种科学和工程应用。第二部分将致力于开发新的高维计算数值方法，重点关注高维数值积分和高维偏微分方程问题。该项目的总体愿景是开发一个框架，用于构建和分析一般非线性随机偏微分方程的数值方法，并开发新的方法和实现方法来克服高维计算的维数挑战。该项目将包括研究生培训。该研究项目开发非线性随机偏微分方程和高维计算的先进数值方法。这样一个及时而先进的项目引起了 STEM 社区的极大兴趣，因为预期的数值方法和算法将提供急需的支持工具，用于解决通过随机偏微分方程进行数学描述或涉及许多科学、工程和工业应用以及人工智能、机器学习和数据科学的高维计算的挑战性问题。该研究项目预计还将对数值随机偏微分方程和高维计算的进步产生持久影响。此外，该项目将为培养博士提供宝贵的机会和资源。研究生，并帮助他们发展必要的应用和计算数学以及人工智能、机器学习和数据科学知识和技能，以便他们能够在不久的将来在学术界或工业界追求成功的职业生涯。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。