III: Small: Collaborative Research: Learning Active Physics-Based Models from Data

III：小：协作研究：从数据中学习基于物理的主动模型

• 批准号: 2008915
• 负责人: Yin Yang
• 金额: $ 25万
• 依托单位: University of Utah
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-07-01 至 2024-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2008915&HistoricalAwards=false
• 关键词: III; Small; Collaborative; Research; Learning

This project explores a novel algorithmic framework for automatic generation of digital models of objects from our natural world, that faithfully reproduce the structure and function of their physical counterparts. We specifically focus on modeling active deformable objects, i.e., objects capable of producing internal forces within their own bodies, such as biological muscles or robotic actuators. Our approach differs from the traditional modeling pipeline by learning the digital models from example data of the mechanism in-action, rather than by manually engineering them from the first principles. We adapt current state-of-the-art deep learning techniques to our problem, in particular artificial neural networks, by endowing them with knowledge about the physics-based behavior of deformable materials. This is expected to significantly upgrade the capabilities of generic neural networks, which would be otherwise forced to learn the laws of physics from data, which is an unnecessary task because fundamental properties of deformable media, such as conservation of energy and rotational invariance, should simply be taken for granted. The proposed algorithmic framework will greatly simplify the creation of digital replicas of objects in our natural world, while enhancing their fidelity. This will empower Virtual and Augmented Reality deployments to deliver life-like experiences in educational and skill-training applications, such as virtual operating rooms or emergency response scenarios. Computer-hosted doubles of functional objects are also a valuable prototyping tool in the design and optimization of physical functional replicas, such as prosthetic devices.To achieve these goals, we hybridize a neural network with a differentiable simulator, which outputs the quasistatic (i.e. equilibrated) shape of an active elastic model as a function of input control parameters, and subject to prescribed (known) boundary conditions. The finite element-based simulator is based on Projective Dynamics and designed with differentiability in mind, which is a key feature that will enable smooth combination with the classical backpropagation algorithm and integration within existing deep learning frameworks, such as PyTorch. The input to the simulator allows the actuation controls to be prescribed at very fine granularity, potentially enabling each finite element to become its own independently controllable actuator. These fine-grained actuation controls will be generated by a convolutional neural network, which creates them using a low-dimensional time-varying control vector and constant (i.e., time-invariant) network weights. We train this aggregate pipeline, jointly inferring both the weights of the control network as well as the values of the latent variables associated with different input configurations, as to best explain the training set as the action of a low-dimensional control space. This core framework will subsequently be extended to 1) allow for processing of contact and collisions, 2) optimization of spatially-varying material parameters, 3) lifting the quasi-statics assumption and simulating time-varying dynamics.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.

该项目探索了一种新型算法框架，用于自动生成我们自然世界的数字模型，从而忠实地重现其物理对应物的结构和功能。我们特别专注于建模有效的可变形物体，即能够在其身体内部产生内力的物体，例如生物肌肉或机器人致动剂。我们的方法与传统的建模管道不同，通过从机制插件的示例数据中学习数字模型，而不是通过从第一原则中进行工程来进行工程。我们通过了解有关可变形材料的基于物理学的行为的知识，将当前最新的深度学习技术适应我们的问题，尤其是人工神经网络。预计这将大大升级通用神经网络的能力，否则，这将被迫从数据中学习物理定律，这是一项不必要的任务，因为可以简单地将可变形介质的基本属性（例如能量和旋转不变性保护）授予。提出的算法框架将大大简化自然界中对象的数字复制品的创建，同时增强其忠诚度。这将赋予虚拟和增强现实部署的能力，以在教育和技能培训应用中提供类似生活的体验，例如虚拟手术室或紧急响应场景。功能对象的计算机固定双打也是一种有价值的原型制作工具，用于设计和优化物理功能复制品（例如假体设备）。为了实现这些目标，我们将神经网络与可区分的模拟器融合在一起，该神经网络输出了标准的（即平衡），即已知的弹性模型，即已知的弹性模型，以范围符合Infuncort of Funcorter of Input andput andput andput andput andput参数。基于有限元的模拟器基于投影动力学，并考虑到可不同的性能，这是一个关键功能，它将能够与经典的反向传播算法和集成在现有的深度学习框架（例如Pytorch）中的平稳组合。对模拟器的输入允许在非常细的粒度下开处方驱动控件，从而有可能使每个有限元素成为其自身独立控制的执行器。这些细颗粒的致动控制将由卷积神经网络生成，该网络使用低维时时间变化的控制向量和常数（即，时间不变）网络权重创建它们。我们训练此总管道，共同推断控制网络的权重以及与不同输入配置相关的潜在变量的值，以最好地解释训练集作为低维控制空间的作用。此核心框架随后将扩展到1）允许处理接触和碰撞的处理，2）优化空间变化的物质参数，3）提升准统计的假设并模拟时间变化的动态。该奖项反映了NSF的法定任务，并通过评估该基金会的智力效果，并通过评估了基金会的范围。