Collaborative Research: Knowledge Guided Machine Learning: A Framework for Accelerating Scientific Discovery

机器学习（ML）在许多可获得大规模数据的应用中的成功，导致人们越来越期待在科学学科中取得类似的成就。数据科学的使用在涉及尚未完全理解的过程的科学问题中特别有前途。然而，一个纯粹的数据驱动的方法来建模一个物理过程可能是有问题的。例如，它可以创建一个复杂的模型，该模型既不能在训练数据之外推广，也不能在物理上解释。当没有足够的训练数据时，这个问题变得更糟，这在科学和工程领域中非常常见。 基于可解释理论的机器学习模型更有可能防止从导致不可推广性能的数据中学习虚假模式。这在处理关键问题和与高风险相关的问题时尤其重要（例如，极端天气或生态系统崩溃）。 因此，无论是ML的，也不是科学知识的唯一的方法可以被认为是足够的知识发现在复杂的科学和工程应用。该项目正在开发新技术，以探索基于知识的模型和ML模型之间的连续性，其中科学知识和数据协同集成。这种综合方法有可能在一系列科学和工程学科中加速发现。该项目将培养精通这些方法的跨学科科学家，并将通过同行评审的出版物、开源软件和一系列研讨会传播项目成果，以吸引更广泛的科学界参与。该项目旨在开发一个框架，利用数据科学模型的独特能力，从数据中自动学习模式和模型，而不忽视积累的科学知识的宝藏。具体而言，该项目通过探索将科学知识和机器学习模型结合在一起的几种方法，建立了知识引导机器学习（KGML）的基础，这些方法使用来自四个领域的试点应用程序：水生生态动力学，气候和天气，水文学和转化生物学。之所以选择这些试点应用程序，是因为它们正处于知识引导的机器学习可以产生变革性影响的临界点。 KGML有潜力为科学家和工程师提供对他们感兴趣领域的新见解，并将需要开发创新的新机器学习方法和架构，以融入科学原理。科学知识，KGML方法，在这个项目中开发的软件可能会扩展到广泛的科学应用，该项目是美国国家科学基金会利用数据革命（HDR）项目的一部分大创意活动。该奖项反映了NSF的法定使命，并通过使用基金会的知识产权进行评估，被认为值得支持。优点和更广泛的影响审查标准。

The success of machine learning (ML) in many applications where large-scale data is available has led to a growing anticipation of similar accomplishments in scientific disciplines. The use of data science is particularly promising in scientific problems involving processes that are not completely understood. However, a purely data-driven approach to modeling a physical process can be problematic. For example, it can create a complex model that is neither generalizable beyond the data on which it was trained nor physically interpretable. This problem becomes worse when there is not enough training data, which is quite common in science and engineering domains. A machine learning model that is grounded by explainable theories stands a better chance at safeguarding against learning spurious patterns from the data that lead to non-generalizable performance. This is especially important when dealing with problems that are critical and associated with high risks (e.g., extreme weather or collapse of an ecosystem). Hence, neither an ML-only nor a scientific knowledge-only approach can be considered sufficient for knowledge discovery in complex scientific and engineering applications. This project is developing novel techniques to explore the continuum between knowledge-based and ML models, where both scientific knowledge and data are integrated synergistically. Such integrated methods have the potential for accelerating discovery in a range of scientific and engineering disciplines. This project will train interdisciplinary scientists who are well versed in such methods and will disseminate results of the project via peer-reviewed publications, open-source software, and a series of workshops to engage the broader scientific community.This project aims to develop a framework that uses the unique capability of data science models to automatically learn patterns and models from data, without ignoring the treasure of accumulated scientific knowledge. Specifically, the project builds the foundations of knowledge-guided machine learning (KGML) by exploring several ways of bringing scientific knowledge and machine learning models together using pilot applications from four domains: aquatic ecodynamics, climate and weather, hydrology, and translational biology. These pilot applications were selected because they are at tipping points where knowledge-guided machine learning can have a transformative effect. KGML has the potential for providing scientists and engineers with new insights into their domains of interest and will require the development of innovative new machine learning approaches and architectures that can incorporate scientific principles. Scientific knowledge, KGML methods, and software developed in this project could potentially be extended to a wide range of scientific applications where mechanistic (also known as process-based) models are used.This project is part of the National Science Foundation's Harnessing the Data Revolution (HDR) Big Idea activity.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.