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: 2026710
• 负责人: Anuj Karpatne
• 金额: $ 5.45万
• 依托单位: Virginia Polytechnic Institute and State University
• 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=2026710&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) 继续给视觉、语音和文本识别等商业领域带来革命性的变化，科学界对释放 ML 的力量以加速科学发现抱有巨大的期望。然而，仅依赖训练数据而忽略现有科学知识的黑盒机器学习模型在科学问题上取得的成功有限，特别是当标记数据有限时，有时甚至会导致巨大的失败。这是因为黑盒 ML 模型很容易学习虚假关系，这些关系在它们所训练的数据之外无法很好地泛化。物理引导机器学习 (PGML) 的新兴范式利用 ML 算法的独特能力，在物理学（或科学理论）中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 有潜力将 ML 转变为黑盒应用之外的解决方案，方法是提供以下解决方案： 通过将机器学习方法与科学知识体系相结合，即使对于与训练期间遇到的不同的未见过的输入输出分布也能很好地概括。 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 模型是独立开发的，但很少混合在一起。拟议的项目与试图将机器学习和领域科学结合起来的现有研究机构有根本的不同，例如，通过以简单的方式在机器学习算法中使用特定于领域的知识，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。该项目中数据科学与物理和传感领域之间的紧密相互作用自然而然地适合多样化的教育活动，这些活动补充了我们团队概述的研究任务。在这个为期一年的项目期间，该团队将开发一门关于“机器学习与物理学”的研究生综合课程，该课程探索这个跨学科领域的热门、新兴主题。该课程将利用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”，每年为宾厄姆顿都会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，类似的外展活动将在学校（洛厄尔的罗宾逊中学和哥伦布的 Metro STEM 中学）进行复制。 PI 致力于增加受该项目影响的培训生态系统各个层面的参与多样性，并计划了各种协调一致的更广泛的影响活动，以包容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价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

PID-GAN: A GAN Framework based on a Physics-informed Discriminator for Uncertainty Quantification with Physics

• DOI: 10.1145/3447548.3467449
• 发表时间: 2021-06
• 期刊: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining
• 作者: Arka Daw;M. Maruf;A. Karpatne

Learning Compact Representations of Neural Networks using DiscriminAtive Masking (DAM)

• 发表时间: 2021-10
• 作者: Jie Bu;Arka Daw;M. Maruf;A. Karpatne

CoPhy-PGNN: Learning Physics-guided Neural Networks with Competing Loss Functions for Solving Eigenvalue Problems

• DOI: 10.1145/3530911
• 发表时间: 2022-12-01
• 期刊: ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY
• 影响因子: 5
• 作者: Elhamod, Mohannad;Bu, Jie;Karpatne, Anuj
• 通讯作者: Karpatne, Anuj