Cover Image

Knowledge-data fusion oriented traffic state estimation: A stochastic physics-informed deep learning approach

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Wang, Ting, Li, Ye, Cheng, Rongjun, Zou, Guojian, Dantsujic, Takao, Ngoduy, Dong
Format:	Preprint
Published:	2024
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Physics-informed deep operator network for traffic state estimation
by: Li, Zhihao, et al.
Published: (2025)

Potential failures of physics-informed machine learning in traffic flow modeling: theoretical and experimental analysis
by: Lei, Yuan-Zheng, et al.
Published: (2025)

Adaptive trajectory-constrained exploration strategy for deep reinforcement learning
by: Wang, Guojian, et al.
Published: (2023)

Adaptive traffic signal safety and efficiency improvement by multi objective deep reinforcement learning approach
by: Mirbakhsh, Shahin, et al.
Published: (2024)

Self-induced stochastic resonance: A physics-informed machine learning approach
by: Savaliya, Divyesh, et al.
Published: (2025)

Interpretable long-term traffic modelling on national road networks using theory-informed deep learning
by: Li, Yue, et al.
Published: (2026)

Towards a pretrained deep learning estimator of the Linfoot informational correlation
by: Berg, Stéphanie M. van den, et al.
Published: (2025)

A comparative study of deep learning and ensemble learning to extend the horizon of traffic forecasting
by: Zheng, Xiao, et al.
Published: (2025)

An approach of deep reinforcement learning for maximizing the net present value of stochastic projects
by: Xu, Wei, et al.
Published: (2025)

Exploring the impact of traffic signal control and connected and automated vehicles on intersections safety: A deep reinforcement learning approach
by: Karbasi, Amir Hossein, et al.
Published: (2024)

A virtual sensor fusion approach for state of charge estimation of lithium-ion cells
by: Previtali, Davide, et al.
Published: (2025)

Comparing statistical and deep learning techniques for parameter estimation of continuous-time stochastic differentiable equations
by: Sankoh, Aroon, et al.
Published: (2025)

Inferring Heterogeneous Treatment Effects of Crashes on Highway Traffic: A Doubly Robust Causal Machine Learning Approach
by: Li, Shuang, et al.
Published: (2024)

Pathology-genomic fusion via biologically informed cross-modality graph learning for survival analysis
by: Zhang, Zeyu, et al.
Published: (2024)

Scalable physics-informed deep generative model for solving forward and inverse stochastic differential equations
by: Zhou, Shaoqian, et al.
Published: (2025)

On-site estimation of battery electrochemical parameters via transfer learning based physics-informed neural network approach
by: Yeregui, Josu, et al.
Published: (2025)

Real-time system optimal traffic routing under uncertainties -- Can physics models boost reinforcement learning?
by: Ke, Zemian, et al.
Published: (2024)

Integrating Newton's Laws with deep learning for enhanced physics-informed compound flood modelling
by: Radfar, Soheil, et al.
Published: (2025)

Ensemble learning of the atrial fiber orientation with physics-informed neural networks
by: Magaña, Efraín, et al.
Published: (2024)

Deep Ritz method with Fourier feature mapping: A deep learning approach for solving variational models of microstructure
by: Mema, Ensela, et al.
Published: (2025)

Goal-oriented learning of stochastic dynamical systems using error bounds on path-space observables
by: Zou, Joanna, et al.
Published: (2026)

Learning and discovering multiple solutions using physics-informed neural networks with random initialization and deep ensemble
by: Zou, Zongren, et al.
Published: (2025)

Multi-view mid fusion: a universal approach for learning in an HDLSS setting
by: Houthuys, Lynn
Published: (2025)

Controlling dynamics of stochastic systems with deep reinforcement learning
by: Mukhamadiarov, Ruslan
Published: (2025)

Audio2Rig: Artist-oriented deep learning tool for facial animation
by: Arcelin, Bastien, et al.
Published: (2024)

Bridging ocean wave physics and deep learning: Physics-informed neural operators for nonlinear wavefield reconstruction in real-time
by: Ehlers, Svenja, et al.
Published: (2025)

Certified machine learning: A posteriori error estimation for physics-informed neural networks
by: Hillebrecht, Birgit, et al.
Published: (2022)

An information field theory approach to Bayesian state and parameter estimation in dynamical systems
by: Hao, Kairui, et al.
Published: (2023)

Partially stochastic deep learning with uncertainty quantification for model predictive heating control
by: Hannula, Emma, et al.
Published: (2025)

Physics-informed deep learning for infectious disease forecasting
by: Qian, Ying, et al.
Published: (2025)

Utilising physics-guided deep learning to overcome data scarcity
by: Bai, Jinshuai, et al.
Published: (2022)

Field-scale soil moisture estimated from Sentinel-1 SAR data using a knowledge-guided deep learning approach
by: Yu, Yi, et al.
Published: (2025)

Training deep physical neural networks with local physical information bottleneck
by: Wang, Hao, et al.
Published: (2026)

A physics-informed and attention-based graph learning approach for regional electric vehicle charging demand prediction
by: Qu, Haohao, et al.
Published: (2023)

Filtered Partial Differential Equations: a robust surrogate constraint in physics-informed deep learning framework
by: Zhang, Dashan, et al.
Published: (2023)

Asymmetrical estimator for training encapsulated deep photonic neural networks
by: Wang, Yizhi, et al.
Published: (2024)

KDH-CAD: Knowledge-data hybrid CAD learning under data scarcity
by: Gao, Ziqin, et al.
Published: (2026)

GT-CausIn: a novel causal-based insight for traffic prediction
by: Gao, Ting, et al.
Published: (2022)

Practical multi-fidelity machine learning: fusion of deterministic and Bayesian models
by: Yi, Jiaxiang, et al.
Published: (2024)

Physics-informed deep-learning applications to experimental fluid mechanics
by: Eivazi, Hamidreza, et al.
Published: (2022)