III: Medium: Advancing Deep Learning for Inverse Modeling

在地球、环境科学和工程等科学学科中，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，水文模型用于预测流域的流量，了解水循环，预测洪涝和干旱，以及制定水库放水等业务决策。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，在溪流的例子中，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，这些参数往往不为人所知或难以近似。逆建模是一种科学方法，它涉及从物理系统的观测结果中反向工作，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建模方法通常需要花费太多时间进行计算，并且无法有效地利用大陆和全球尺度上日益增加的可用数据量。该项目的目标是为环境科学应用中的逆建模开发新一代机器学习算法，该算法可以利用大型数据集在决策相关尺度上提供改进的预测和不确定性估计，同时显着减少所需的计算量。这些方法将广泛适用于健康、环境、农业和工程等不同学科，因此具有解决重大社会挑战的潜力。该项目旨在开发一种基于嵌入式的机器学习框架，用于逆建模，该框架适用于广泛的科学问题，其目标是在给定驱动程序和响应数据的情况下识别系统的显式或隐含特征。这一提出的方法将引入创新来解决诸如数据稀疏性、空间异质性、处理数据不确定性的需求以及处理不同规模和保真度数据的能力等挑战。神经过程方法家族将取得方法上的进步，使它们能够模拟涉及多个具有多个输入和输出的多个相互作用过程的物理系统。这些神经过程模型还将纳入科学知识，如守恒定律，以及隐含在基于过程的模型中的知识，以推广到样本外场景。将开发一种新的方法，通过基于深潜变量方法的生成模型来提高对物理系统的过程级理解。将开发一种基于实体固有特征的图神经网络方法来学习实体之间的亲和力，同时将科学的领域知识注入到关系中。将使用贝叶斯深度学习来估计和减轻不确定性，以获得更好的可解释性和更好的输入嵌入的分布恢复。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.