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

CDS

• 批准号: 2244953
• 负责人: Lin Cheng
• 金额: $ 28.77万
• 依托单位: Worcester Polytechnic Institute
• 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=2244953&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，以利用符号人工智能 (AI) 自动发现软功能复合材料的本构定律。软功能复合材料对于可拉伸和可穿戴电子产品、软机器人以及传感和驱动等新兴技术和经济领域至关重要。传统的宪法法发现方法通常耗时、低效，而且对于复杂的案件往往效果不佳。该项目将利用符号人工智能技术，不仅适用于软功能复合材料，还适用于其他材料类别，从而显着加速本构律发现过程。开发的软件工具将通过网站向研究和教育界提供。该项目还将培训本科生和研究生，包括人工智能、力学和材料科学跨学科领域的本科生和研究生，包括代表性不足的群体和劳动力。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定律，涉及广义张量本构定律的函数，基于标量和张量的算子，以及噪声数据的统计分析。该项目将使用改进的表示和对称理论建立不同各向异性材料本构定律的一般张量函数。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的基础上，并将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规律。该工具及其提供的见解将加快软功能复合材料以及最终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。