CAREER: Learning and Information Processing in Non-linear Mechanical Systems

职业：非线性机械系统中的学习和信息处理

• 批准号: 2239801
• 负责人: Arvind Murugan
• 金额: $ 60万
• 依托单位: University of Chicago
• 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: 2023-01-15 至 2027-12-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2239801&HistoricalAwards=false
• 关键词: CAREER; Learning; Information; Processing; Non

NONTECHNICAL SUMMARY This CAREER award will support research to understand how mechanical systems can learn intelligent behavior in ways similar to neural networks. Mechanical materials will be studied that actively re-organize themselves through local processes so as to recognize and respond to subtle patterns in physical stimuli. The fields of condensed matter physics and materials science have traditionally focused on systems with fixed capabilities. Inspired by the brain, this project seeks to understand the principles that allow physical systems to change themselves and `learn’ new functions. The brain is remarkable not only in its ability to learn but in that learning is done without any global coordinator who decides which neurons should connect to whom in order to recognize a friendly face. Instead, neurons wire up with each other through local processes, such as Hebbian learning (‘fire together, wire together’), and self-organize to recognize patterns in the world around us. Inspired by such local self-organization in service of global function, the PI recently proposed training a material in using Hebbian-like learning to reinforce right behaviors and anti-Hebbian learning to penalize wrong behaviors. The ability to eliminate undesired behaviors in addition to enhancing desired ones will allow for a powerful physical analog of supervised learning in neural networks. However, a material cannot autonomously learn in this Hebbian and anti-Hebbian manner without special forms of memory. Further, fundamental principles of thermodynamics dictate that these forms of memory and their erasure require materials to consume energy. This project will investigate such non-equilibrium requirements for Hebbian-inspired learning in materials. The consideration of non-equilibrium phenomena is at the frontier of condensed matter physics, but instead of order and pattern formation, this project focuses on non-equilibrium information processing. Besides developing the underlying theory, this project will investigate how feedback loops between mechanics and chemistry can lead to non-equilibrium learning behaviors in practice. The PI will also investigate what physical aspects make a mechanical system act like a neural network, i.e., be capable of recognizing complex patterns in stimuli. This work will provide insight into how biological systems adapt to their environment since feedback between mechanics and chemistry is common in cell biology. At a fundamental level, this project will expand our view of what non-equilibrium mechanical systems can do beyond mechanical responses to learning behaviors like neural networks. Undergraduate and graduate students will be trained in interdisciplinary research. The PI will develop courses that target the emerging scientific interface between physics and materials science. For the broader public, the PI will develop hands-on demos that illustrate and relate to cutting-edge ideas in both physics and theoretical computer science. These demos will include 3-D printed networks and mechanical structures that change and adapt as they are used. They will communicate to the broader public how exciting new research emerges at the interface of different sciences. The demos will be disseminated through outreach events and to K-12 educators for use in the classroom. TECHNICAL SUMMARY The goal of this proposal is to elucidate the non-equilibrium requirements for mechanical systems to physically learn new functionalities in a way rivaling neural networks. While many existing frameworks for adaptive materials can enhance a desired behavior by lowering its energy during a period of training, these frameworks are missing key ingredients that we will address here. Unlike artificial neural networks that are trained by global optimization, biological neural networks are thought to learn through local rules such as Hebbian learning. Since physical systems are also typically constrained by locality, the PI has explored training a material in a supervised way by using Hebbian learning when it shows the right behaviors and anti-Hebbian learning when it shows the wrong behaviors. The ability to eliminate undesired behaviors in addition to enhancing desired ones will allow for a powerful physical analog of supervised learning in neural networks. However, for a material to learn autonomously through such Hebbian and anti-Hebbian learning, it must have forms of memory that are only allowed when detailed balance is broken. This project will determine the non-equilibrium memory requirements for supervised learning in matter that experiences desired and undesired behaviors over time. In addition to abstract theory, this project will also explore how natural chemo-mechanical feedback circuits, found in numerous biological and synthetic systems, can implement such memory. Through collaborations with materials scientists, this project will instantiate the theory here in specific systems. This work will expand our understanding of what non-linear non-equilibrium disordered mechanical systems can do. While non-equilibrium driving has been extensively studied as a mechanism of order, organization and pattern formation, our framework shows how non-equilibrium driving enables a system to learn statistical stimuli-response behaviors, much like a neural network. A frontier in soft condensed matter is to reveal the universe of behaviors enabled by chemo-mechanical coupling; but developments are currently siloed with sophisticated neural network-like information processing investigated in well-mixed molecular circuits without any mechanics. This work will build a unified theoretical framework, combining statistical physics models of molecular circuits with adaptive mechanical networks, allowing us to compute the dissipation requirements for adaptive behaviors in mechanical systems. Finally, by contrasting learning in mechanical systems and in neural networks, this work will provide a new perspective on learning itself. The proposed theoretical condensed matter work will develop tools that will impact materials engineering, including metamaterials and bio-materials, and biological physics. While current approaches optimize for desired behaviors chosen ahead of time, the new framework here allows materials to autonomously learn and improve function over time by harnessing recent advances ranging from DNA nanotechnology to ceramics and alloys. The interdisciplinary research here is also an ideal platform for impacting high school education through engaging demos based on 3-d printed meta-materials and shape-memory alloys. These demos will be disseminated through public science outreach events in Chicago.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.

非技术总结本职业奖将支持研究，以了解机械系统如何以类似于神经网络的方式学习智能行为。将研究机械材料，通过局部过程积极地重组自己，以识别并应对物理刺激中的微妙模式。传统上，冷凝物质物理和材料科学领域一直集中在具有固定功能的系统上。受大脑的启发，该项目试图理解允许物理系统改变自己并“学习”新功能的原则。大脑不仅在学习能力方面都很出色，而且在没有任何全球协调员的情况下完成学习的能力，他们决定哪些神经元应该与谁建立联系以识别友好的面孔。取而代之的是，神经元通过本地过程（例如Hebbian学习（“一起火”，汇合在一起”），并自组织以识别我们周围世界的模式。受到这种本地自组织为全球功能服务的启发，PI最近提出了一种培训一种材料，以使用HEBBIAN样的学习来增强正确的行为和反Hebbian学习来惩罚错误的行为。除了增强所需的行为外，消除UNESED行为的能力将允许在神经网络中进行有力的监督学习的物理模拟。但是，如果没有特殊形式的记忆，材料就无法以这种赫比安和反海比亚的方式自主学习。此外，热力学的基本原理表明，这些形式的记忆及其擦除需要材料消耗能量。该项目将调查针对材料中Hebbian启发的学习的这种非平衡要求。对非平衡现象的考虑是在凝结物质物理的边界，但该项目不再是秩序和模式形成，而是侧重于非平衡信息处理。除了发展基本理论外，该项目还将研究机械和化学之间的反馈回路如何导致实践中的非平衡学习行为。 PI还将研究哪些物理方面使机械系统的作用像神经网络，即能够识别刺激中的复杂模式。这项工作将提供有关生物系统如何适应其环境的洞察力，因为机械和化学之间的反馈在细胞生物学中很常见。在基本层面上，该项目将扩大我们对非平衡机械系统可以做什么的看法，而不是对神经网络等学习行为的机械响应。本科生和研究生将接受跨学科研究的培训。 PI将开发针对物理学和材料科学之间新兴科学界面的课程。对于更广泛的公众来说，PI将开发动手演示，这些演示可以说明物理学和理论计算机科学中的尖端思想。这些演示将包括3D打印网络和机械结构，这些网络会随着使用而改变和适应。他们将向更广泛的公众沟通，在不同科学的界面上出现了令人兴奋的新研究。演示将通过外展活动和K-12教育者在课堂上使用。技术摘要该提案的目的是阐明机械系统的非平衡要求，以一种风险的神经网络来物理学习新功能。尽管许多现有的自适应材料框架可以通过在培训期间降低其能量来增强所需的行为，但这些框架缺少我们将在此处解决的关键因素。与通过全球优化培训的人工神经网络不同，人们认为生物神经网络可以通过当地规则（例如Hebbian学习）学习。由于物理系统通常也受到当地的限制，因此PI在显示正确的行为时使用HEBBIAN学习时，通过使用HEBBIAN学习来探索材料，当它显示出错误的行为时，它可以显示出正确的行为和反赫比亚的学习。除了增强所需的行为外，消除未经保证的行为的能力将允许在神经网络中进行有力的监督学习。但是，要使材料通过这样的Hebbian和反海比亚学习自主学习，它必须具有只有在详细平衡被打破时才能允许的记忆形式。该项目将确定监督学习的非平衡记忆要求，因为经历的经验和不希望的行为随着时间的流逝。除了抽象理论外，该项目还将探讨在许多生物学和合成系统中发现的天然化学机械反馈电路如何实现这种记忆。通过与材料科学家的合作，该项目将在此处实例化特定系统中的理论。这项工作将扩大我们对非线性非平衡机械系统可以做什么的理解。尽管非平衡驾驶已被广泛研究为秩序，组织和模式形成的机制，但我们的框架表明了非平衡驱动器如何使系统能够学习统计刺激刺激反应行为，就像神经元网络一样。软凝结物质中的边界是揭示通过化学机械耦合实现的行为宇宙。但是，目前，在混合良好的分子电路中研究了没有任何机械的分子电路中研究的复杂神经网络样信息处理。这项工作将建立一个统一的理论框架，将分子电路的统计物理模型与自适应机械网络相结合，从而使我们能够计算机械系统中适应性行为的耗散要求。最后，通过将机械系统和神经网络中的学习进行对比，这项工作将为学习本身提供新的观点。拟议的理论凝结材料工作将开发将影响材料工程的工具，包括超材料和生物材料以及生物物理学。尽管当前的方法可针对提前选择的所需行为进行优化，但这里的新框架允许材料通过利用从DNA纳米技术到陶瓷和合金的最新进展来自主学习和提高功能。这里的跨学科研究也是通过基于3-D打印的元材料和形状 - 记忆合金的演示来影响高中教育的理想平台。这些演示将通过芝加哥的公共科学宣传活动传播。该奖项反映了NSF的法定使命，并通过使用基金会的知识分子优点和更广泛的影响评估标准来评估，被认为是珍贵的支持。