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

该职业奖将支持研究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。将研究通过局部过程主动重新组织自身的机械材料，以便识别和响应物理刺激中的微妙模式。凝聚态物理和材料科学领域传统上关注的是具有固定能力的系统。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”新功能的原理。大脑的非凡之处不仅在于它的学习能力，还在于它的学习是在没有任何全局协调者的情况下完成的，这些协调者决定哪些神经元应该连接到谁身上，以识别一张友好的脸。相反，神经元通过局部过程相互连接，比如Hebbian学习（“一起激发，一起连接”），并自我组织以识别我们周围世界的模式。受这种服务于全球功能的本地自组织的启发，PI最近提出了一种训练材料，使用类赫比学习来强化正确的行为，使用反赫比学习来惩罚错误的行为。除了增强期望行为外，消除不希望的行为的能力将使神经网络中监督学习的强大物理模拟成为可能。然而，如果没有特殊形式的记忆，材料就不能以这种赫比式和反赫比式的方式自主学习。此外，热力学的基本原理表明，这些形式的记忆及其消除需要材料消耗能量。这个项目将研究这种非平衡要求，以赫比式的方式学习材料。非平衡现象的考虑是凝聚态物理的前沿，但本项目侧重于非平衡信息处理，而不是秩序和模式的形成。除了发展基础理论外，该项目还将研究力学和化学之间的反馈回路如何在实践中导致非平衡学习行为。PI还将研究使机械系统像神经网络一样运作的物理方面，即能够识别刺激中的复杂模式。这项工作将深入了解生物系统如何适应环境，因为力学和化学之间的反馈在细胞生物学中很常见。在基本层面上，这个项目将扩展我们对非平衡机械系统的看法，超越对神经网络等学习行为的机械反应。本科生和研究生将接受跨学科研究方面的培训。PI将开发针对物理学和材料科学之间新兴科学接口的课程。对于更广泛的公众，PI将开发动手演示，说明和联系物理学和理论计算机科学的前沿思想。这些演示将包括3d打印网络和机械结构，这些结构可以随着使用而改变和适应。他们将向更广泛的公众传达不同科学的交叉点上出现的令人兴奋的新研究。这些演示将通过外展活动传播给K-12教育工作者，供他们在课堂上使用。本提案的目标是阐明机械系统以与神经网络相抗衡的方式物理学习新功能的非平衡要求。虽然许多现有的自适应材料框架可以通过在训练期间降低其能量来增强期望的行为，但这些框架缺少我们将在这里讨论的关键成分。与通过全局优化训练的人工神经网络不同，生物神经网络被认为是通过局部规则（如Hebbian学习）进行学习。由于物理系统通常也受到局部性的限制，PI已经探索了以监督的方式训练材料，当它显示正确的行为时使用Hebbian学习，当它显示错误的行为时使用反Hebbian学习。除了增强期望行为外，消除不希望的行为的能力将使神经网络中监督学习的强大物理模拟成为可能。然而，对于一个材料来说，要通过这种赫比式和反赫比式的学习来自主学习，它必须有一种记忆形式，这种形式只有在细节平衡被打破时才被允许。该项目将确定监督学习的非平衡记忆要求，即随着时间的推移经历期望和不期望的行为。除了抽象理论之外，该项目还将探索在众多生物和合成系统中发现的自然化学-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，该项目将在特定系统中实例化该理论。这项工作将扩展我们对非线性非平衡无序机械系统的理解。虽然非平衡驱动作为一种秩序、组织和模式形成的机制已被广泛研究，但我们的框架显示了非平衡驱动如何使系统能够学习统计刺激反应行为，就像神经网络一样。软凝聚态物质的一个前沿是揭示化学-力学耦合使行为的宇宙；但是，目前的发展仅限于在没有任何力学的情况下，在混合良好的分子电路中研究复杂的类似神经网络的信息处理。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，使我们能够计算机械系统中自适应行为的耗散要求。最后，通过对比机械系统和神经网络中的学习，这项工作将为学习本身提供一个新的视角。提出的理论凝聚态工作将开发工具，将影响材料工程，包括超材料和生物材料，以及生物物理学。虽然目前的方法是针对提前选择的预期行为进行优化，但这里的新框架允许材料自主学习，并通过利用从DNA纳米技术到陶瓷和合金的最新进展，随着时间的推移提高功能。这里的跨学科研究也是一个理想的平台，通过基于3d打印超材料和形状记忆合金的引人入胜的演示来影响高中教育。这些演示将通过芝加哥的公共科学推广活动进行传播。该奖项反映了美国国家科学基金会的法定使命，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。

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.