Using deep learning to extend the range of air pollution monitoring and forecasting

DOI10.1016/j.jcp.2020.109278OpenAlexW3003037705WikidataQ126308714 ScholiaQ126308714MaRDI QIDQ2123348

Julien Monteil, Fearghal O'Donncha, Jakub Mareček, Philipp Hähnel

Publication date: 8 April 2022

Published in: Journal of Computational Physics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1810.09425

zbMATH Keywords

air pollutionpartial differential equationsdeep learning

Mathematics Subject Classification ID

Biology and other natural sciences (92-XX) Computer science (68-XX)

Uses Software

• FLUENT
• TensorFlow
• PointNet
• CALPUFF
• torchdiffeq
• PDE-Net
• DGM

Cites Work

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• Smoluchowski's coagulation equation: Uniqueness, nonuniqueness and a hydrodynamic limit for the stochastic coalescent
• Non-intrusive reduced order modeling of nonlinear problems using neural networks
• Machine learning of linear differential equations using Gaussian processes
• Hidden physics models: machine learning of nonlinear partial differential equations
• Data-driven reduced order modeling for time-dependent problems
• DGM: a deep learning algorithm for solving partial differential equations
• Non-intrusive reduced order modeling of unsteady flows using artificial neural networks with application to a combustion problem
• Localised sequential state estimation for advection dominated flows with non-Gaussian uncertainty description
• Data driven governing equations approximation using deep neural networks
• Detecting troubled-cells on two-dimensional unstructured grids using a neural network
• PDE-Net 2.0: learning PDEs from data with a numeric-symbolic hybrid deep network
• An artificial neural network as a troubled-cell indicator
• Neural algorithm for solving differential equations
• A Survey of Projection-Based Model Reduction Methods for Parametric Dynamical Systems
• A Taylor-Galerkin method for convective transport problems
• The Mathematics of Atmospheric Dispersion Modeling
• Hyperbolic systems of conservation laws II
• The Lagrangian Relaxation Method for Solving Integer Programming Problems
• General Black-Scholes models accounting for increased market volatility from hedging strategies
• Survey of Multifidelity Methods in Uncertainty Propagation, Inference, and Optimization
• Isogeometric Analysis
• Solving high-dimensional partial differential equations using deep learning
• Learning representations by back-propagating errors
• On kinematic waves II. A theory of traffic flow on long crowded roads
• Approximation by superpositions of a sigmoidal function

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