CAREER: Accelerating Scientific Discovery via Deep Learning with Strong Physics Inductive Biases

职业：通过具有强物理归纳偏差的深度学习加速科学发现

• 批准号: 2338909
• 负责人: Kookjin Lee
• 金额: $ 59.61万
• 依托单位: Arizona State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-09-01 至 2029-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2338909&HistoricalAwards=false
• 关键词: CAREER; Accelerating; Scientific; Discovery; via

Scientific discovery--the process of gaining new knowledge about the natural world from empirical observations of complex physical processes--has long been a topic of research. While historical approaches often necessitated the lifelong collection of data and analysis of talented scholars, modern computing technologies have revolutionized methods of scientific discovery. Today, advanced computing machines and machine learning (ML) facilitate the rapid analysis of large-scale complex data. However, current approaches often rely on opaque "black-box" ML models. They suffer from a lack of physically-consistency, generalizability, and/or interpretability. To address these limitations, this CAREER project focuses on establishing a synergistic research and education program to advance methods of scientific discovery. The core research idea of the project is to embed physics domain knowledge into the design of deep-learning (DL) models. By augmenting strong physics-based model designs with carefully formulated hypotheses and massive data collections, this approach will produce novel DL models that enforce the principles of physics to strongly restrict search spaces of model outcomes to be physically consistent. This innovative approach is expected to greatly accelerate the process of scientific discovery and enhance its efficiency, accuracy, and interpretability. The expected outcome of this project is also widely applicable to relevant research areas such as Thermodynamics and Molecular dynamics.This project investigates data-driven scientific discovery of complex spatiotemporal physical processes. It aims to achieve the following specific goals: to develop (1) physically-consistent dynamics models, which exactly preserve important physical characteristics by imposing strong physics biases to the design of neural networks; (2) DL-based symbolic regression algorithms to infer interpretable mathematical expressions of target dynamics models, where the model structures are designed to conform to principles of physics; and, (3) multimodal DL algorithms for scientific applications that leverage data that exist in different modalities. In addition, this project seeks to develop (4) physics-inspired DL models for data science downstream tasks by leveraging findings from the above tasks, contributing to ML research. An interdisciplinary course will be developed based on the project outcomes to educate researchers, students at all levels, and the broader communities. Lastly, this project offers research opportunities for students, prioritizing recruitment from underrepresented groups.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.

科学发现 - 从对复杂物理过程的经验观察获得有关自然世界的新知识的过程 - 长期以来一直是研究的话题。尽管历史方法通常需要终身收集数据和才华横溢的学者分析，但现代计算技术已经彻底改变了科学发现的方法。如今，高级计算机和机器学习（ML）有助于大规模复杂数据的快速分析。但是，当前的方法通常依赖不透明的“黑盒” ML模型。他们患有缺乏身体上一致，普遍性和/或解释性。为了解决这些局限性，该职业项目着重于建立协同研究和教育计划，以推进科学发现方法。该项目的核心研究思想是将物理领域知识嵌入深度学习（DL）模型的设计中。通过使用精心构造的假设和大量数据收集来增强强大的基于物理的模型设计，该方法将产生新型的DL模型，以强制实施物理原理，以强烈限制模型结果的搜索空间在物理上是一致的。这种创新的方法有望极大地加速科学发现的过程，并提高其效率，准确性和解释性。该项目的预期结果也广泛适用于相关的研究领域，例如热力学和分子动力学。该项目研究了复杂时空物理过程的数据驱动的科学发现。它的目的是实现以下特定目标：开发（1）身体上一致的动力学模型，该模型通过对神经网络的设计施加强大的物理偏见来确切地保留重要的物理特征； （2）基于DL的符号回归算法推断目标动力学模型的可解释数学表达式，其中模型结构旨在符合物理原理； （3）用于利用不同方式存在的数据的科学应用的多模式DL算法。此外，该项目旨在通过利用上述任务的发现来开发（4）个物理启发的DL模型，用于下游任务，从而有助于ML研究。将根据项目成果开发跨学科课程，以教育研究人员，各级学生以及更广泛的社区。最后，该项目为学生提供了研究机会，优先考虑了代表性不足的小组的招聘。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛的影响评估标准通过评估来支持的。