CDS&E: Deep Spring: a Neural Network-based Approach to Design of Slender Structures

CDS

• 批准号: 2053971
• 负责人: Vwani Roychowdhury
• 金额: $ 75万
• 依托单位: University of California-Los Angeles
• 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: 2021-07-01 至 2024-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2053971&HistoricalAwards=false
• 关键词: CDS& Deep; Spring; Neural; Network

Slender structures are vital to our lives – from columns and beams that support buildings, to airplane wings and blood vessels. By understanding how they respond to strain and the factors that give rise to bending and buckling, engineers can design safer and more efficient systems with applications in robotics, civil engineering, and medical treatment. Many systems with slender structures exhibit complex behavior that is non-linear and counter intuitive. This Computational and Data-Enabled Science and Engineering (CDS&E) project combines machine learning and physics-based modeling to better understand their behavior and how to harness it to provide new capabilities. The findings will enable new structures such as soft robots with new forms of motion and control for medical procedures or search and rescue activities. The knowledge gained may also enable the design of products such as stretchable electronics for health care, bio sensing and a broad range of consumer and industrial products. The project will develop a website and software repository to share the simulation tools with the research community. Educational materials including instructional videos will be developed for graduate courses and K-12 classes.This project will enable the design of new materials and structures like soft kirigami composites and compressive buckling-induced micro-sized 3D architecture materials. The researchers will advance numerical modeling of systems with slender structures through a combination of machine learning and discrete differential geometry, using “nonlinear springs” represented by neural networks to simulate the systems. The enhanced computational speed of this approach will aid in the design and optimization of engineering systems such as deployable structures and soft robots. Developing predictive models for slender structures and metamaterials is challenging because of the inherent instabilities and many possible configurations. If successful, this development would be the first of its kind in the mechanics research community and would provide the twin capability to predict complex structural responses and design structures with an inverse problem solver. While the target proof-of-concept examples relate to slender structures and metamaterials, the computational methods would be generalizable to a broad class of material-structure systems.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.

细长的结构对我们的生活至关重要-从支撑建筑物的柱子和横梁到飞机机翼和血管。 通过了解它们对应变的反应以及引起弯曲和屈曲的因素，工程师可以设计出更安全，更高效的系统，应用于机器人，土木工程和医疗。许多具有细长结构的系统表现出非线性和反直觉的复杂行为。这个计算和数据支持的科学与工程（CDS E）项目结合了机器学习和基于物理的建模，以更好地了解它们的行为以及如何利用它来提供新的功能。 这些发现将使新的结构，如软机器人，具有新的运动和控制形式，用于医疗程序或搜索和救援活动。所获得的知识也可以使产品的设计，如可拉伸电子医疗保健，生物传感和广泛的消费和工业产品。该项目将开发一个网站和软件库，与研究界分享模拟工具。该项目将为研究生课程和K-12课程开发包括教学视频在内的教育材料。该项目将使新材料和结构的设计成为可能，如软kirigami复合材料和压缩屈曲诱导的微型3D建筑材料。研究人员将通过机器学习和离散微分几何的结合来推进细长结构系统的数值建模，使用神经网络代表的“非线性弹簧”来模拟系统。 这种方法提高了计算速度，将有助于工程系统的设计和优化，如可展开结构和软机器人。由于细长结构和超材料固有的不稳定性和许多可能的配置，开发预测模型是具有挑战性的。如果成功的话，这将是力学研究界的第一次，并将提供预测复杂结构响应和设计结构的双重能力。虽然目标概念验证的例子涉及细长结构和超材料，计算方法将推广到广泛的材料结构系统。该奖项反映了NSF的法定使命，并已被认为是值得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的支持。

项目成果

A Fully Implicit Method for Robust Frictional Contact Handling in Elastic Rods

• DOI: 10.1016/j.eml.2022.101924
• 发表时间: 2022-05
• 期刊: ArXiv
• 作者: Dezhong Tong;Andrew Choi;Jungseock Joo;M. Jawed

Design of Bistable Soft Deployable Structures via a Kirigami‐Inspired Planar Fabrication Approach

• DOI: 10.1002/admt.202300088
• 发表时间: 2023-01
• 期刊: Advanced Materials Technologies
• 影响因子: 6.8
• 作者: Mrunmayi Mungekar;Leixin Ma;Wenzhong Yan;Vishal Kackar;Shyan Shokrazadeh;M. Jawed

Rapidly encoding generalizable dynamics in a Euclidean symmetric neural network

• DOI: 10.1016/j.eml.2022.101925
• 发表时间: 2022-03
• 期刊: Extreme Mechanics Letters
• 影响因子: 4.7
• 作者: Qiaofeng Li;Tianyi Wang;V. Roychowdhury;M. Jawed

Snap Buckling in Overhand Knots

反手结中的卡扣屈曲

• DOI: 10.1115/1.4056478
• 发表时间: 2023
• 期刊: Journal of Applied Mechanics
• 作者: Tong, Dezhong;Choi, Andrew;Joo, Jungseock;Borum, Andy;Khalid Jawed, Mohammad
• 通讯作者: Khalid Jawed, Mohammad

Ballooning in Spiders using Multiple Silk Threads

• DOI: 10.1103/physreve.105.034401
• 发表时间: 2021-12
• 期刊: Physical review. E
• 作者: C. Habchi;M. Jawed