CAREER: Unifying Scientific Knowledge with Machine Learning for Forward, Inverse, and Hybrid Modeling of Scientific Systems

职业：将科学知识与机器学习相结合，对科学系统进行正向、逆向和混合建模

• 批准号: 2239328
• 负责人: Anuj Karpatne
• 金额: $ 59.57万
• 依托单位: Virginia Polytechnic Institute and State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-07-01 至 2028-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2239328&HistoricalAwards=false
• 关键词: CAREER; Unifying; Scientific; Knowledge; Machine

One of the fundamental goals in science is to build mathematical models of scientific systems that can explain the nature of the physical world by predicting the system's behavior. Current standards of science-based models, rooted in scientific theories and equations, suffer from several shortcomings in modeling complex real-world systems. At the core of these shortcomings is their theoretical scientific nature that restricts them from making effective use of data that is not well-described theoretically. Consequently, machine learning methods, that can automatically extract patterns and relationships from data, are increasingly being viewed as promising alternatives to science-based models. However, black-box machine learning models, that solely rely on information contained in data and are agnostic to scientific theories, have met with limited success in scientific problems. Instead, there is a growing realization to unify scientific knowledge with machine learning in the emerging field of knowledge-guided machine learning. This project aims to make novel advances in knowledge-guided machine learning in the context of three driving use-cases: fluid dynamics, aerosol modeling, and lake modeling. A central goal of this project is to prepare the next generation of workforce in science and engineering comprising of a diverse cadre of students who can easily cross disciplinary boundaries between machine learning and scientific fields. This project will also have direct impacts to science and society through the three real-world use-cases and through collaborations with industry partners. The long-term vision of this project is to establish knowledge-guided machine learning as a full-fledged research and education discipline for the advancement of science. This project aims to make novel advances in three primary research tasks of knowledge-guided machine learning: forward modeling with scientific equations and data, inverse modeling for inferring parameters in science-based models, and hybrid-science-machine learning modeling to remove imperfections in science-based models. This project will contribute transformative innovations in knowledge-guided machine learning for incorporating a wide variety of scientific knowledge in machine learning frameworks, from partial differential equations in fluid dynamics to numerical models in aerosol modeling and phenomenological rules in lake modeling. In the task of forward modeling, this project will develop a new class of algorithms in science-guided curriculum learning to exploit the interplay between data-driven and scientific supervision while training deep learning models. This project will also develop novel science-guided resampling strategies for generating scientifically consistent predictions during inference. In the task of inverse modeling, this project will lead to novel formulations of knowledge-guided inverse modeling, where scientific supervision (in terms of knowledge of the forward model) is used to guide the training of machine learning-based inverse models. In the task of hybrid modeling, this project will result in a new class of residual correcting neural networks for augmenting systematic biases or residuals in science-based outputs, and methods to jointly infer parameters of science-based models while correcting for residuals in their outputs. Beyond the three use-cases, the methodologies developed in this project can potentially impact a number of scientific disciplines where scientific knowledge and models are routinely used.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.

科学的基本目标之一是建立科学系统的数学模型，这些模型可以通过预测系统的行为来解释物理世界的性质。植根于科学理论和方程式的当前基于科学的模型的当前标准在建模复杂的现实世界系统时遇到了几个缺点。这些缺点的核心是他们的理论科学性质，限制了他们有效利用理论上描述的数据。因此，可以自动从数据中提取模式和关系的机器学习方法越来越被视为基于科学模型的有前途的替代方法。但是，仅依靠数据中包含的信息并不可知科学理论的黑盒机器学习模型在科学问题上取得了有限的成功。取而代之的是，在新兴的知识引导机器学习领域中，通过机器学习统一科学知识的认识越来越大。该项目旨在在三个驾驶用例的背景下在知识引导的机器学习方面取得新的进步：流体动力学，气溶胶建模和湖泊建模。该项目的一个核心目标是准备科学和工程学领域的下一代劳动力，包括各种各样的学生，他们可以轻松地在机器学习和科学领域之间跨越学科的界限。该项目还将通过三个现实世界的用例以及与行业合作伙伴的合作对科学和社会产生直接影响。该项目的长期愿景是建立知识引导的机器学习，作为一门成熟的研究和教育学科，以实现科学的发展。该项目旨在在知识引导的机器学习的三个主要研究任务中取得新的进步：使用科学方程和数据进行前向建模，用于推断基于科学模型的参数的逆建模以及混合科学计算机学习模型，以消除基于科学模型的缺陷。该项目将在知识引导的机器学习中贡献变革性创新，以在机器学习框架中纳入各种科学知识，从流体动力学的部分微分方程到气溶胶建模中的数值模型和湖泊建模中的现象学规则。在正向建模的任务中，该项目将在科学指导的课程学习中开发新的算法，以在培训深度学习模型时利用数据驱动和科学监督之间的相互作用。该项目还将开发出新颖的科学指导重采样策略，以在推论过程中产生科学一致的预测。在反向建模的任务中，该项目将导致知识引导的逆建模的新颖表述，其中科学监督（就向前模型的知识而言）用于指导基于机器学习的逆模型的培训。在混合建模的任务中，该项目将导致一类新的残留纠正神经网络，以增加基于科学的输出中的系统偏见或残留物，以及共同推断基于科学的模型的参数，同时纠正其输出中的残留物。除了这三个用例之外，该项目中开发的方法可能会影响许多科学知识和模型的科学学科。该奖项反映了NSF的法定任务，并且认为值得通过基金会的知识分子优点评估来支持，并具有更广泛的影响。