EAGER: Collaborative Research: III: Exploring Physics Guided Machine Learning for Accelerating Sensing and Physical Sciences

随着机器学习(ML)继续给包括视觉、语音和文本识别在内的商业空间带来革命性的变化，科学界对释放机器学习加速科学发现的力量寄予了极大的期望。然而，仅依赖训练数据和更多现有科学知识的黑盒最大似然模型在科学问题上取得的成功有限，特别是在标记数据有限的情况下，有时甚至导致惊人的失败。这是因为黑盒ML模型很容易学习虚假关系，这些关系不能很好地概括它们所训练的数据之外的东西。物理制导的机器学习(PGML)是一种新兴的机器学习范式，它利用ML算法的独特能力，在物理(或科学理论)积累的知识的指导下，自动从数据中提取模式和模型，旨在解决黑盒ML在科学应用中面临的挑战。对于数据科学，PGML具有将ML转换为黑盒应用的潜力，通过使解决方案能够很好地泛化即使在看不见的输入-输出分布上也能很好地泛化，而不是在训练期间遇到的那些不同的输入-输出分布，通过将ML方法与科学知识主体固定在一起。PGML与传统的基于物理的模型和ML模型是孤立发展的，但很少混合在一起的观点有了明显的不同。拟议的项目与现有的试图将ML和领域科学相结合的研究机构有根本的不同，例如，通过以简化的方式利用ML算法中特定于领域的知识，或者在基于物理的建模过程中利用数据，尽管不允许数据改变现有基于物理的模型的函数形式。数据科学与项目中的物理和传感领域之间的紧密相互作用自然有助于开展各种教育活动，补充我们团队概述的研究任务。在这个为期一年的项目期间，该小组将开发一门研究生水平的综合课程--“MLMeet物理学”，探索这一跨学科领域的热门、新兴主题。课程的提供将利用四所大学之间共享的课程模块，如共享的客座视频和案例研究。北卡罗来纳大学物理系有一个精心设计的“物理推广项目”，该项目每年都会为宾厄姆顿大都市区的小学举办科学展览，为此，该团队将制作一个关于神经网络和ML的新展览。在后续工作中，类似的越野活动将在学校复制(洛厄尔的罗宾逊中学和哥伦布的Metro STEM中学)。PIS致力于在受该项目影响的培训生态系统的各个层面增加参与的多样性，并计划了各种协调的更广泛的影响活动，以纳入女性和代表性不足的少数族裔学生以及教师。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

As machine learning (ML) continues to revolutionize the commercial space including vision, speech, andtext recognition, there is a huge anticipation in the scientific community to unlock the power of ML foraccelerating scientific discovery. However, black-box ML models, which rely solely on training data andignore existing scientific knowledge have met with limited success in scientific problems, particularlywhen labeled data is limited, sometimes even leading to spectacular failures. This is because the blackbox ML models are susceptible to learning spurious relationships that do not generalize well outside thedata they are trained for. The emerging paradigm of physics-guided machine learning (PGML), whichleverages the unique ability of ML algorithms to automatically extract patterns and models from data withguidance of the knowledge accumulated in physics (or scientific theories), aims to address the challengesfaced by black box ML in scientific applications.For data science, PGML has the potential to transform ML beyond black-box applications by enablingsolutions that generalize well even on unseen input-output distributions that are different from thoseencountered during training, by anchoring ML methods with the scientific body of knowledge. PGML makes a distinctdeparture from the conventional view that physics-based models and ML models are developed inisolation but seldom mixed together. The proposed project is fundamentally different from existing bodyof research that attempts to combine ML and domain sciences, e.g., by making use of domain-specificknowledge in ML algorithms in simplistic ways, or making use of data in the physics-based modelingprocess albeit without allowing data to change the functional forms of existing physics-based models.The tight interplay between data science and the domains of physics and sensing in the project lends itselfnaturally to diverse education activities that complement the research tasks outlined by our team. Over theduration of this one-year project, the team will develop an integrative course at the graduate level on "MLmeets Physics", which explores topical, emerging themes in this interdisciplinary area. Offerings of thecourse will draw upon course modules shared between the four universities, such as shared guest videosand case studies. The physics department at BU has a well-developed "Physics Outreach Project" thatannually performs science exhibitions for elementary schools in Binghamton metropolitan area, for whichthe team will create a new exhibition about neural networks and ML. In follow-on work, similar outreachevents will be replicated at schools (Robinson Middle School in Lowell and Metro STEM Middle Schoolin Columbus). The PIs are committed to increasing the diversity of involvement at various levels of thetraining ecosystem impacted by this project, and have planned various coordinated broader impactactivities for inclusion of female and underrepresented minority students as well as 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.