Deep learning solver for solving advection-diffusion​ equation in comparison to finite difference methods

TL;DR: This study introduces Deep-BCSI, a deep learning framework for bias correction and spatial imputation of PM2.5 concentrations in South Korea, achieving higher accuracy and reduced RMSE in metropolitan areas, with potential applications in operational facilities and policymaking.

TL;DR: Lagrangian dynamic mode decomposition with a locally time-invariant approximation of the Koopman operator is combined to ameliorate challenges of discovery of mathematical descriptors of physical phenomena from observational and simulated data.

TL;DR: This paper introduces a novel framework that integrates physical priors into diffusion-based generative models, enabling the efficient generation of physically realistic dynamics, including trajectories and flows, with remarkable robustness and accuracy across various physical phenomena.

TL;DR: This study proposes a physics-guided graph convolutional deep equilibrium network for environmental data modeling, incorporating physical laws into a graph-based DEQ model, and demonstrates improved performance in air quality estimation tasks using real-world data.

TL;DR: In this article, the authors introduce physics-informed neural networks, which are trained to solve supervised learning tasks while respecting any given laws of physics described by general nonlinear partial differential equations.

TL;DR: This study explores how Deep Learning can be utilized for addressing some important problems in Big Data Analytics, including extracting complex patterns from massive volumes of data, semantic indexing, data tagging, fast information retrieval, and simplifying discriminative tasks.

TL;DR: The Models-3 CMAQ system as mentioned in this paper is a community multiscale air quality modeling system that includes a meteorological modeling system for the description of atmospheric states and motions, emission models for man-made and natural emissions that are injected into the atmosphere, and a chemistry-transport modelling system for simulation of the chemical transformation and fate.

TL;DR: The Advanced Research WRF (ARW) model is described, representative of this generation and of a class of models using explicit time-splitting integration techniques to efficiently integrate the Euler equations, and is the first fully compressible conservative-form nonhydrostatic atmospheric model suitable for both research and weather prediction applications.