CDS&E/Collaborative Research: A Symbolic Artificial Intelligence Framework for Discovering Physically Interpretable Constitutive Laws of Soft Functional Composites

CDS

• 批准号: 2244952
• 负责人: Pu Zhang
• 金额: $ 29.5万
• 依托单位: SUNY at Binghamton
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-06-01 至 2026-05-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2244952&HistoricalAwards=false
• 关键词: CDS& Collaborative; Research; Symbolic; Artificial

This Computational and Data-Enabled Science and Engineering (CDS&E) collaborative research grant supports fundamental research to enable automatic discovery of constitutive laws for soft functional composites using symbolic artificial intelligence (AI). Soft functional composites are essential to emerging technological and economic areas such as stretchable and wearable electronics, soft robotics, and sensing and actuation. Conventional methods for constitutive law discovery are usually time-consuming, inefficient, and often ineffective for complex cases. This project will significantly accelerate the constitutive law discovery process by leveraging symbolic AI technology not only for soft functional composites but also other material classes. The software tools developed will be made available to the research and education communities through a website. This project will also train undergraduate and graduate students, including underrepresented groups, workforce in the transdisciplinary area of AI, mechanics, and materials science.The project will establish a symbolic AI framework for discovering physically interpretable constitutive laws of soft functional composites, involving general tensor functions of constitutive laws, both scalar and tensor-based operators, and statistical analysis for noisy data. The project will establish general tensor functions of constitutive laws for different anisotropic materials using modified representation and symmetry theories. The symbolic-AI architecture will be built upon genome representation, physics constraints, symbolic tree search, and statistic modeling, and will be applied to discover and interpret mechano-physical constitutive laws of soft functional composites with various microstructures. The tool and the insights it provides will expedite the design and applications of soft functional composites, and eventually other material classes.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.

这项计算和数据支持的科学与工程（CDS E）合作研究资助支持基础研究，以便使用符号人工智能（AI）自动发现软功能复合材料的本构律。软功能复合材料对于新兴的技术和经济领域至关重要，例如可拉伸和可穿戴电子产品，软机器人以及传感和驱动。传统的本构律发现方法通常耗时、效率低下，而且对于复杂的情况往往无效。该项目将通过利用符号人工智能技术，不仅用于软功能复合材料，还用于其他材料类别，从而显着加快本构律发现过程。开发的软件工具将通过一个网站提供给研究和教育界。该项目还将培训本科生和研究生，包括代表性不足的群体，人工智能，力学和材料科学跨学科领域的劳动力。该项目将建立一个符号人工智能框架，用于发现软功能复合材料的物理可解释本构律，涉及本构律的一般张量函数，标量和基于张量的算子，以及噪声数据的统计分析。本计画将利用修正的表象与对称性理论，建立不同异向性材料本构关系的一般张量函数。符号人工智能架构将建立在基因组表示，物理约束，符号树搜索和统计建模，并将被应用于发现和解释具有各种微观结构的软功能复合材料的机械物理本构关系。该工具及其提供的见解将加快软功能复合材料的设计和应用，并最终加快其他材料类别的设计和应用。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。