Combining Physics and Machine Learning for Network Flow Estimation
The flow estimation problem consists of predicting missing edge flows in a network (e.g., traffic, power and water) based on partial observations. These missing flows depend both on the underlying physics (edge features and a flow conservation law) as well as the observed edge flows. This paper introduces an optimization framework for computing missing flows and solves the problem using bilevel optimization and deep learning. Empirical results show that the method accurately predicts missing flows, outperforming the best baseline by up to 20%, and is able to capture relevant physical properties in traffic and power networks.
Code (0)
등록된 구현이 없습니다.
Tasks
BIG-bench Machine LearningBilevel OptimizationSimilar Papers 제목 키워드 기반
Physics-informed graph neural networks for flow field estimation in carotid arteries
Hemodynamic quantities are valuable biomedical risk factors for cardiovascular pathology such as atherosclerosis. Non-invasive, in-vivo measurement of these quantities can only be performed using a select number of modal…
Macroscopic Traffic Flow Modeling with Physics Regularized Gaussian Process: Generalized Formulations
Despite the success of classical traffic flow (e.g., second-order macroscopic) models and data-driven (e.g., Machine Learning - ML) approaches in traffic state estimation, those approaches either require great efforts fo…
BIG-bench Machine LearningState EstimationStochastic OptimizationFlows for Flows: Morphing one Dataset into another with Maximum Likelihood Estimation
Many components of data analysis in high energy physics and beyond require morphing one dataset into another. This is commonly solved via reweighting, but there are many advantages of preserving weights and shifting the …
MORPHMacroscopic Traffic Flow Modeling with Physics Regularized Gaussian Process: A New Insight into Machine Learning Applications
Despite the wide implementation of machine learning (ML) techniques in traffic flow modeling recently, those data-driven approaches often fall short of accuracy in the cases with a small or noisy dataset. To address this…
Bayesian InferenceBIG-bench Machine LearningStochastic OptimizationTrafficFlowGAN: Physics-informed Flow based Generative Adversarial Network for Uncertainty Quantification
This paper proposes the TrafficFlowGAN, a physics-informed flow based generative adversarial network (GAN), for uncertainty quantification (UQ) of dynamical systems. TrafficFlowGAN adopts a normalizing flow model as the …
Generative Adversarial NetworkState EstimationUncertainty Quantification