III: Medium: Advancing Deep Learning for Inverse Modeling

III：媒介：推进逆向建模的深度学习

• 批准号: 2313174
• 负责人: Vipin Kumar
• 金额: $ 120万
• 依托单位: University of Minnesota-Twin Cities
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-01 至 2026-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2313174&HistoricalAwards=false
• 关键词: III; Medium; Advancing; Deep; Learning

In scientific disciplines, such as earth and environmental sciences and engineering, researchers use models to understand complex systems and make predictions about future states or behaviors. For example, hydrology models are used for the prediction of streamflow in the river basin and for understanding water cycles, predicting floods and droughts, and making operational decisions such as reservoir release. The models of these physical systems often depend on a number of parameters (characteristics) that describe the system. In the streamflow example, for instance, the important characteristics include slope, land cover, and soil type. However, for a wide variety of reasons, these parameters are often not known or poorly approximated. Inverse modeling is a type of scientific method that involves working backward from observations of a physical system to estimate the parameters and identify the underlying processes or mechanisms that could have produced the observed behavior. Existing approaches for inverse modeling used in the physical science community often take too much time to compute and are unable to effectively leverage increasing amounts of data becoming available at continental and global scales. The goal of this project is to develop a new generation of machine learning algorithms for inverse modeling in environmental science applications that can leverage large datasets to provide improved prediction and uncertainty estimation at decision-relevant scales while significantly reducing the amount of computation required. These methods will have wide applicability in disciplines as diverse as health, environment, agriculture, and engineering, and thus have the potential to address major societal challenges.This project aims to develop an embedding-based machine learning framework for inverse modeling that is applicable to a wide range of scientific problems where the goal is to identify explicit or implicit characteristics of a system given its drivers and response data. This proposed methodology will introduce innovations to address challenges such as data sparsity, spatial heterogeneity, the need to handle uncertainty in data, and the ability to work with data at different scales and fidelity. Method advancements will be made to the family of neural process methods so that they can model physical systems involving multiple interacting processes with multiple inputs and outputs. These neural process models will also incorporate scientific knowledge, such as conservation laws, as well as knowledge implicit in process-based models to generalize to out-of-sample scenarios. A new approach will be developed to improve the process-level understanding of physical systems via a generative model based on deep latent variable methods. A graph neural network approach will be developed to learn the affinity among entities based on their inherent characteristics while also injecting scientific domain knowledge into the relationships. Method advancements will be made to estimate and mitigate uncertainty using Bayesian deep learning for better explainability and for obtaining better distributional recovery of the input embeddings.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.

在地球、环境科学和工程学等科学学科中，研究人员使用模型来理解复杂的系统，并对未来的状态或行为做出预测。例如，水文学模型被用来预测河流流域的径流，了解水循环，预测洪水和干旱，并做出诸如水库泄洪等业务决策。这些物理系统的模型通常依赖于描述系统的许多参数(特征)。例如，在径流中，重要的特征包括坡度、土地覆盖和土壤类型。然而，由于各种各样的原因，这些参数通常是未知的或不太接近的。逆向建模是一种科学方法，它涉及从对物理系统的观察向后工作，以估计参数，并确定可能产生观察到的行为的潜在过程或机制。物理科学界使用的现有逆向建模方法往往需要花费太多的时间进行计算，并且无法有效地利用大陆和全球范围内越来越多的可用数据。该项目的目标是开发新一代机器学习算法，用于环境科学应用中的逆向建模，该算法可以利用大数据集在决策相关的尺度上提供更好的预测和不确定性估计，同时显著减少所需的计算量。这些方法将在健康、环境、农业和工程等领域具有广泛的适用性，因此有可能解决主要的社会挑战。该项目旨在开发一个基于嵌入式的机器学习框架，用于反向建模，适用于广泛的科学问题，目标是识别给定驱动因素和响应数据的系统的显性或隐式特征。这一拟议的方法将引入创新，以应对诸如数据稀疏性、空间异质性、需要处理数据中的不确定性以及处理不同比例和保真度的数据的能力等挑战。将对神经过程方法家族进行方法改进，以便它们能够对涉及具有多个输入和输出的多个相互作用过程的物理系统进行建模。这些神经过程模型还将结合科学知识，如守恒定律，以及基于过程的模型中隐含的知识，以推广到样本外的情况。将开发一种新的方法，通过基于深度潜变量方法的产生式模型来提高对物理系统的过程级别的理解。将开发一种图形神经网络方法，以根据实体的固有特征学习实体之间的亲和力，同时还将科学领域知识注入到关系中。为了更好地解释和获得更好的输入嵌入的分布恢复，将使用贝叶斯深度学习来估计和减少不确定性。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。