Collaborative proposal: Workshop on Numerical Modeling with Neural Networks, Learning, and Multilevel Finite Element Methods

合作提案：神经网络数值建模、学习和多级有限元方法研讨会

• 批准号: 2132710
• 负责人: Ludmil Zikatanov
• 金额: $ 3万
• 依托单位: Pennsylvania State Univ University Park
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-11-01 至 2022-10-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2132710&HistoricalAwards=false
• 关键词: Collaborative; proposal; Workshop; Numerical; Modeling

The workshop will focus on the development, investigation, and application of fundamental mathematical theories and advanced modeling techniques using both Artificial Intelligence (AI) tools, such as deep neural networks, and scientific computing techniques, such as multilevel finite element methods. Participation of students, postdocs, junior, and senior researchers in the workshop creates an effective environment for exchange of ideas between researchers working in areas related to machine learning and scientific computing. The overarching goal of this workshop is to bridge these communities with the aim of creating efficient and more robust methods and techniques for the use of AI in mathematical and computational modeling of phenomena in physics, biology, and the social sciences. The workshop will be driven by several factors. The first is the wide range of numerical and mathematical models for real-time interactions at different scales. The second is the combination of numerical tools using classical discretizations with neural network techniques that can handle complex models in physical, biological, and medical research beyond current scientific computing capabilities. The workshop will also include topics in robust linear and nonlinear solvers for different temporal and spatial scales and efficient and interpretable learning processes that provide physics-aware numerical models. Finally, topics on advanced software packages tuned to modern high performance computers will be included. The workshop will be held at Pennsylvania State University, University Park, PA on November 3-5, 2021 (https://jxu60.math.psu.edu).A primary objective of the workshop is to marshal forces to work on the development of newly integrated techniques that properly address the fundamental difficulties of solving multiphysics and multiscale problems. Workshop participants will have the opportunity to present their research related to scientific machine learning and traditional numerical methods, communicate and interact with leading experts from other countries, and initiate potential collaborations. The workshop focuses on key open questions in neural networks, machine learning, and multilevel methods, and is expected to lead to new ideas and collaborations in the development of analytic results and simulation technologies. On a longer-time scale, the design of the workshop will help to enable future advances in interdisciplinary studies of machine learning and traditional scientific computing, which will facilitate progress in many other areas of science.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.

研讨会将侧重于使用人工智能(AI)工具(如深度神经网络)和科学计算技术(如多层有限元方法)开发、研究和应用基本数学理论和高级建模技术。学生、博士后、初级和高级研究人员参加研讨会，为从事机器学习和科学计算相关领域的研究人员之间的思想交流创造了一个有效的环境。该研讨会的总体目标是在这些社区之间架起桥梁，目的是创建高效和更强大的方法和技术，以便在物理、生物和社会科学中使用人工智能对现象进行数学和计算建模。研讨会将受到几个因素的推动。首先是针对不同尺度的实时交互的广泛的数值和数学模型。第二种是使用经典离散化的数值工具与神经网络技术的结合，这种技术可以处理物理、生物和医学研究中超出当前科学计算能力的复杂模型。讲习班还将包括不同时间和空间尺度的稳健线性和非线性解算器以及提供物理感知数值模型的有效和可解释的学习过程的主题。最后，将包括与现代高性能计算机相适应的高级软件包的主题。研讨会将于2021年11月3日至5日在宾夕法尼亚州宾夕法尼亚州立大学的大学公园举行(https://jxu60.math.psu.edu).A研讨会的主要目标是组织力量致力于开发新的集成技术，适当地解决解决多物理和多尺度问题的根本困难。研讨会参与者将有机会介绍他们在科学机器学习和传统数值方法方面的研究，与其他国家的领先专家交流和互动，并启动潜在的合作。研讨会的重点是神经网络、机器学习和多水平方法中的关键开放问题，并有望在分析结果和模拟技术的开发方面带来新的想法和合作。在更长的时间范围内，研讨会的设计将有助于促进机器学习和传统科学计算的跨学科研究的未来进步，这将促进许多其他科学领域的进步。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。