Learning for Dynamics and Control Workshop, To Be Held in Boston, MA, May 30-31, 2019

在过去的几年里，机器学习在计算机视觉和语言翻译等许多领域产生了巨大的影响。在未来十年，最大的数据生成器预计将是感知和控制物理世界的设备。这种来自物理世界的实时数据爆炸需要机器学习，基于模型的动态系统以及控制和决策理论等领域的和解。虽然控制理论已经牢牢扎根于基于模型的设计传统，但数据的可用性和规模（时间和空间）将需要重新思考该学科的基础。从机器学习的角度来看，未来的主要挑战之一是超越模式识别，解决数据驱动的控制和决策问题，以及基于学习的动态过程优化。我们建议在5月30日至31日在麻省理工学院举行为期两天的首届会议，讨论学习，动态和控制之间的接口。从长远来看，我们希望将这一活动作为年度会议，在此展示这一新兴接口的最佳成果。我们的目标是创建一个新的社区，人们在各个学科中进行严格的思考，提出新颖的基本问题，并发展这个新科学领域的基础。我们认为，我们提出的努力将导致一个非常有影响力的年度会议，可以吸引许多科学界的关注。该提案的智力价值是创建一个科学论坛，汇集了控制系统，优化，机器学习和相关学科领域的先驱和最先进的研究，以创建一个着名的年度会议，定义了最先进的动态和控制系统学习。关于这一主题的精英会议不仅可以在科学上弥合两个遥远的领域，而且可以从社区的角度产生巨大的影响，培养越来越多的初级研究人员在这个新兴的界面上工作。一个涵盖控制、优化和学习的精英会议将为学生、博士后和初级教师的专业发展提供一个自然和培育的家园。该奖项反映了NSF的法定使命，并被认为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来支持。

Over the past few years, machine learning has had tremendous impact in numerous areas such as computer vision and language translation. Over the next decade, the biggest generator of data is expected to be devices which sense and control the physical world. This explosion of real-time data that is emerging from the physical world requires a rapprochement of areas such as machine learning, model based dynamical systems, and control and decision theory. While control theory has been firmly rooted in tradition of model-based design, the availability and scale of data (both temporal and spatial) will require rethinking of the foundations for the discipline. From a machine learning perspective, one of the main challenges going forward is to go beyond pattern recognition and address problems in data driven control and decision making as well as learning-based optimization of dynamical processes.We propose a two-day inaugural conference on the interface between learning, dynamics and control to take place on May 30-31 at MIT. Long term we would like to sustain this event as an annual conference where best results on this emerging interface are presented. We aim to create a new community of people that think rigorously across the disciplines, ask novel fundamental questions, and develop the foundations of this new scientific area. We feel that our proposed effort will result in a very influential annual conference that can attract a lot of attention from numerous scientific communities. The intellectual merit of this proposal is the creation of a scientific forum that brings together pioneers and state of the art research in the areas of control systems, optimization, machine learning, and related disciplines to create a prestigious annual conference that defines the state-of-the-art in Learning for Dynamical and Control Systems. An elite conference on this topic can have tremendous impact not only scientifically by bridging two distant areas but also from a community perspective that nurtures a growing number of junior researchers working on this emerging interface. An elite conference across control, optimization and learning will provide a natural and nurturing home for the professional development for students, postdocs, and junior faculty.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.