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

EAGER：协作研究：III：探索物理引导机器学习以加速传感和物理科学

• 批准号: 2026702
• 负责人: Wei-Cheng Lee
• 金额: $ 5.39万
• 依托单位: SUNY at Binghamton
• 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: 2020-05-01 至 2022-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2026702&HistoricalAwards=false
• 关键词: EAGER; Collaborative; Research; III; Exploring

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.

随着机器学习（ML）不断革新商业领域，包括视觉、语音和文本识别，科学界对释放ML的力量来加速科学发现有着巨大的期待。然而，仅仅依赖于训练数据而忽略现有科学知识的黑盒ML模型在科学问题上取得了有限的成功，特别是当标记数据有限时，有时甚至会导致严重的失败。这是因为黑箱ML模型容易学习到虚假的关系，这些关系不能很好地泛化到它们所训练的数据之外。新兴的物理引导机器学习范式（PGML）利用ML算法的独特能力，在物理学（或科学理论）积累的知识的指导下，从数据中自动提取模式和模型，旨在解决黑箱ML在科学应用中面临的挑战。对于数据科学来说，PGML有潜力通过将ML方法与科学知识体系相结合，使解决方案能够很好地泛化，甚至在不可见的输入输出分布上也与训练期间遇到的情况不同，从而使ML超越黑箱应用。PGML与传统观点截然不同，传统观点认为基于物理的模型和ML模型是单独开发的，很少混合在一起。拟议的项目从根本上不同于现有的试图将ML和领域科学结合起来的研究机构，例如，通过以简单的方式使用ML算法中的领域特定知识，或者在基于物理的建模过程中使用数据，尽管不允许数据改变现有基于物理的模型的功能形式。数据科学与项目中物理和传感领域之间的紧密相互作用使其自然地适合于各种教育活动，以补充我们团队概述的研究任务。在为期一年的项目期间，该团队将在研究生阶段开发一门关于“MLmeets Physics”的综合课程，该课程将探索这个跨学科领域的热门和新兴主题。课程内容将借鉴四所大学共享的课程模块，例如共享的客座视频和案例研究。波士顿大学物理系有一个完善的“物理推广项目”，每年为宾厄姆顿大都会地区的小学举办科学展览，团队将为此创建一个关于神经网络和机器学习的新展览。在后续工作中，类似的推广活动将在学校（洛厄尔的罗宾逊中学和哥伦布的Metro STEM中学）进行复制。pi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民族学生以及教师。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。