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影响。将不确定性纳入数学模型是必不可少的，以便从生物学，工程和物理应用中开发出更准确和鲁棒的数学模型，这使得有必要考虑随机部分微分方程，因为它们是最常见的应用程序中最常见的随机模型。为了寻求这些方程式的解决方案需要准确，高效且可靠的计算方法和算法，对这种方法和算法的需求从未有所更大。当前求解随机部分微分方程的方法在大规模面临着相当大的挑战，同时，由于考虑了更加困难的非线性和噪声，因此这种随机模型变得更加复杂。现有的数值方法无法有效地解决这些问题，这反过来又需要新的想法和方法。随着非传统应用科学的快速发展，例如数学金融，图像处理，经济学和数据科学，人们对解决有效的数值方法的需求不断增加，以解决挑战性的高维问题，例如计算集成和求解部分微分方程。传统的基于网格的方法受到臭名昭著的维度诅咒的阻碍，在这种情况下，解决问题所需的计算数量在维度上呈指数增长。该研究项目的主要目标是开发新颖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。第一部分着重于开发和分析有效的数值方法，以解决几种非线性随机偏微分方程，这些方程是由各种科学和工程应用（例如材料科学，流体和量子力学）以及最佳控制产生的。第二部分将致力于开发用于高维计算的新型数值方法，重点是高维数值集成和高维偏微分方程的问题。该项目的过度愿景是开发一个框架，用于构建和分析一般非线性随机部分偏微分方程的数值方法，并开发新方法并促进克服高维计算的维度挑战的诅咒的方法。该项目将包括对研究生的培训。该研究项目开发了用于非线性随机偏微分方程和高维计算的高级数值方法。这种及时，高级的项目对STEM社区引起了极大的兴趣，因为预期的数值方法和算法将提供急需的促成工具，以解决由随机部分微分方程数学上描述的挑战性问题，或者与许多科学，工程以及工业应用以及AI，机器学习和机器学习和数据科学相关的高维计算。预计该研究项目对数值随机部分微分方程和高维计算的进步有持久的影响。此外，该项目将为培训博士学位提供宝贵的机会和资源。研究生并帮助他们开发必要的应用和计算数学以及AI，机器学习以及数据科学知识和技能，以便他们可以在不久的将来从学术界或行业中从事成功的职业。这项奖项反映了NSF的法定任务，并通过基金会的知识分子优点和广泛的影响来评估NSF的法定任务，并被视为值得的支持。