Automated Model Discovery for Soft Matter

软物质的自动模型发现

• 批准号: 2320933
• 负责人: Ellen Kuhl
• 金额: $ 40万
• 依托单位: Stanford University
• 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: 2023-09-01 至 2026-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2320933&HistoricalAwards=false
• 关键词: Automated; Model; Discovery; Soft; Matter

In solid mechanics, a constitutive relation describes a material’s response to external stimuli, such as forces. Constitutive modeling and parameter identification are the cornerstones of the mechanics of materials and structures. The current gold standard in constitutive modeling is to first select a model and then fit its parameters to data. However, the scientific criteria for model selection are poorly understood and depend largely on user experience and personal preference. This award seeks to democratize constitutive modeling through automated model discovery and make it accessible to a more inclusive and diverse community. The main deliverable is an open source discovery platform that will discover the best model and parameters, entirely without human interaction. This open source platform will feature a new family of neural networks, data, models, and parameters. It will be freely available to a wide range of users, regardless of their institutional or financial resources. As such, automated model discovery will lower the barrier of entry into the STEM fields and foster a more inclusive and diverse scientific community. This project has broad scientific, social, and economic impacts. It will democratize constitutive modeling, stimulate discovery in the mechanics of materials and structures, establish machine learning tools to characterize, create, and functionalize soft matter, and train the next generation of civil, mechanical, and manufacturing innovators to use these new technologies. The goal of this research is to establish neural networks that autonomously discover models for soft matter systems. Instead of using classical neural networks that provide no insight into the underlying physics, this project designs its own constitutive neural networks. To train, test, and validate these networks, this project will generate an open source library with benchmark data from dozens of living and engineered materials. All networks, data, models, and parameters of this project will be freely available to promote engineering education and advance scientific knowledge. This project has the potential to induce a paradigm shift in constitutive modeling, from user-defined model selection to automated model discovery. This could forever change how we simulate materials and structures.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.

在固体力学中，本构关系描述了材料对外部刺激(如力)的反应。本构模型和参数识别是材料和结构力学的基石。目前本构建模的黄金标准是首先选择一个模型，然后将其参数与数据进行匹配。然而，人们对模型选择的科学标准知之甚少，在很大程度上取决于用户体验和个人偏好。该奖项旨在通过自动模型发现使本构模型大众化，并使其能够被更具包容性和多样性的社区访问。主要成果是一个开源的发现平台，它将发现最佳的模型和参数，完全不需要人工交互。这个开源平台将以一系列新的神经网络、数据、模型和参数为特色。它将向广泛的用户免费提供，无论他们的机构或财政资源如何。因此，自动模型发现将降低进入STEM领域的门槛，并培养一个更具包容性和多样性的科学界。该项目具有广泛的科学、社会和经济影响。它将使本构模型大众化，促进材料和结构力学的发现，建立机器学习工具来表征、创造软物质并使其功能化，并培训下一代民用、机械和制造创新者使用这些新技术。这项研究的目标是建立神经网络，自动发现软物质系统的模型。该项目没有使用经典的神经网络，而是设计了自己的构成神经网络，而不是提供对潜在物理的洞察。为了训练、测试和验证这些网络，该项目将生成一个开放源代码库，其中包含来自数十种生物和工程材料的基准数据。该项目的所有网络、数据、模型和参数都将免费提供，以促进工程教育和提高科学知识。该项目有可能导致本构模型的范式转变，从用户定义的模型选择到自动模型发现。这可能会永远改变我们模拟材料和结构的方式。这一奖项反映了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。