Physics-Informed Deep Learning For Traffic State Estimation: A Survey and the Outlook

• URL: http://arxiv.org/abs/2303.02063v2
• Date: Sat, 1 Jul 2023 18:59:06 GMT
• Title: Physics-Informed Deep Learning For Traffic State Estimation: A Survey and the Outlook
• Authors: Xuan Di, Rongye Shi, Zhaobin Mo, Yongjie Fu
• Abstract summary: Physics-informed deep learning (PIDL) is a paradigm hybridizing physics-based models and deep neural networks (DNN) One key challenge of applying PIDL to various domains and problems lies in the design of a computational graph that integrates physics and DNNs. In this paper, we provide a variety of architecture designs of PIDL computational graphs and how these structures are customized to traffic state estimation (TSE)
• Score: 7.656272344163666
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: For its robust predictive power (compared to pure physics-based models) and sample-efficient training (compared to pure deep learning models), physics-informed deep learning (PIDL), a paradigm hybridizing physics-based models and deep neural networks (DNN), has been booming in science and engineering fields. One key challenge of applying PIDL to various domains and problems lies in the design of a computational graph that integrates physics and DNNs. In other words, how physics are encoded into DNNs and how the physics and data components are represented. In this paper, we provide a variety of architecture designs of PIDL computational graphs and how these structures are customized to traffic state estimation (TSE), a central problem in transportation engineering. When observation data, problem type, and goal vary, we demonstrate potential architectures of PIDL computational graphs and compare these variants using the same real-world dataset.

Related papers

• Physics-Encoded Graph Neural Networks for Deformation Prediction under Contact [87.69278096528156]
  In robotics, it's crucial to understand object deformation during tactile interactions. We introduce a method using Physics-Encoded Graph Neural Networks (GNNs) for such predictions. We've made our code and dataset public to advance research in robotic simulation and grasping.
  arXiv  Detail & Related papers  (2024-02-05T19:21:52Z)
• Numerical analysis of physics-informed neural networks and related models in physics-informed machine learning [18.1180892910779]
  Physics-informed neural networks (PINNs) have been very popular in recent years as algorithms for the numerical simulation of both forward and inverse problems for partial differential equations. We provide a unified framework in which analysis of the various components of the error incurred by PINNs in approximating PDEs can be effectively carried out.
  arXiv  Detail & Related papers  (2024-01-30T10:43:27Z)
• STDEN: Towards Physics-Guided Neural Networks for Traffic Flow Prediction [31.49270000605409]
  The lack of integration between physical principles and data-driven models is an important reason for limiting the development of this field. We propose a physics-guided deep learning model named Spatio-Temporal Differential Equation Network (STDEN), which casts the physical mechanism of traffic flow dynamics into a deep neural network framework. Experiments on three real-world traffic datasets in Beijing show that our model outperforms state-of-the-art baselines by a significant margin.
  arXiv  Detail & Related papers  (2022-09-01T04:58:18Z)
• Human Trajectory Prediction via Neural Social Physics [63.62824628085961]
  Trajectory prediction has been widely pursued in many fields, and many model-based and model-free methods have been explored. We propose a new method combining both methodologies based on a new Neural Differential Equation model. Our new model (Neural Social Physics or NSP) is a deep neural network within which we use an explicit physics model with learnable parameters.
  arXiv  Detail & Related papers  (2022-07-21T12:11:18Z)
• Scalable algorithms for physics-informed neural and graph networks [0.6882042556551611]
  Physics-informed machine learning (PIML) has emerged as a promising new approach for simulating complex physical and biological systems. In PIML, we can train such networks from additional information obtained by employing the physical laws and evaluating them at random points in the space-time domain. We review some of the prevailing trends in embedding physics into machine learning, using physics-informed neural networks (PINNs) based primarily on feed-forward neural networks and automatic differentiation.
  arXiv  Detail & Related papers  (2022-05-16T15:46:11Z)
• Symmetry Group Equivariant Architectures for Physics [52.784926970374556]
  In the domain of machine learning, an awareness of symmetries has driven impressive performance breakthroughs. We argue that both the physics community and the broader machine learning community have much to understand.
  arXiv  Detail & Related papers  (2022-03-11T18:27:04Z)
• Physically Explainable CNN for SAR Image Classification [59.63879146724284]
  In this paper, we propose a novel physics guided and injected neural network for SAR image classification. The proposed framework comprises three parts: (1) generating physics guided signals using existing explainable models, (2) learning physics-aware features with physics guided network, and (3) injecting the physics-aware features adaptively to the conventional classification deep learning model for prediction. The experimental results show that our proposed method substantially improve the classification performance compared with the counterpart data-driven CNN.
  arXiv  Detail & Related papers  (2021-10-27T03:30:18Z)
• ForceNet: A Graph Neural Network for Large-Scale Quantum Calculations [86.41674945012369]
  We develop a scalable and expressive Graph Neural Networks model, ForceNet, to approximate atomic forces. Our proposed ForceNet is able to predict atomic forces more accurately than state-of-the-art physics-based GNNs.
  arXiv  Detail & Related papers  (2021-03-02T03:09:06Z)
• Physics-Integrated Variational Autoencoders for Robust and Interpretable Generative Modeling [86.9726984929758]
  We focus on the integration of incomplete physics models into deep generative models. We propose a VAE architecture in which a part of the latent space is grounded by physics. We demonstrate generative performance improvements over a set of synthetic and real-world datasets.
  arXiv  Detail & Related papers  (2021-02-25T20:28:52Z)
• A Physics-Informed Deep Learning Paradigm for Car-Following Models [3.093890460224435]
  We develop a family of neural network based car-following models informed by physics-based models. Two types of PIDL-CFM problems are studied, one to predict acceleration only and the other to jointly predict acceleration and discover model parameters. The results demonstrate the superior performance of neural networks informed by physics over those without.
  arXiv  Detail & Related papers  (2020-12-24T18:04:08Z)
• Thermodynamics-based Artificial Neural Networks for constitutive modeling [0.0]
  We propose a new class of data-driven, physics-based, neural networks for modeling of strain rate independent processes at the material point level. The two basic principles of thermodynamics are encoded in the network's architecture by taking advantage of automatic differentiation. We demonstrate the wide applicability of TANNs for modeling elasto-plastic materials, with strain hardening and softening strain.
  arXiv  Detail & Related papers  (2020-05-25T15:56:34Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.