Physics-informed neural networks for the shallow-water equations on the sphere
Published 2022 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
Physics-informed neural networks for the shallow-water equations on the sphere
Authors
Keywords
Physics-informed neural networks, Scientific machine learning, Geophysical fluid dynamics, Shallow-water equations on the sphere
Journal
JOURNAL OF COMPUTATIONAL PHYSICS
Volume 456, Issue -, Pages 111024
Publisher
Elsevier BV
Online
2022-02-10
DOI
10.1016/j.jcp.2022.111024
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- Physics-informed machine learning: case studies for weather and climate modelling
- (2021) K. Kashinath et al. PHILOSOPHICAL TRANSACTIONS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES
- A generative adversarial network approach to (ensemble) weather prediction
- (2021) Alex Bihlo NEURAL NETWORKS
- Parallel physics-informed neural networks via domain decomposition
- (2021) Khemraj Shukla et al. JOURNAL OF COMPUTATIONAL PHYSICS
- Conservative physics-informed neural networks on discrete domains for conservation laws: Applications to forward and inverse problems
- (2020) Ameya D. Jagtap et al. COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
- PPINN: Parareal physics-informed neural network for time-dependent PDEs
- (2020) Xuhui Meng et al. COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
- NSFnets (Navier-Stokes flow nets): Physics-informed neural networks for the incompressible Navier-Stokes equations
- (2020) Xiaowei Jin et al. JOURNAL OF COMPUTATIONAL PHYSICS
- Variational integrator for the rotating shallow-water equations on the sphere
- (2019) Rüdiger Brecht et al. QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY
- Can machines learn to predict weather? Using deep learning to predict gridded 500‐hPa geopotential height from historical weather data
- (2019) Jonathan A. Weyn et al. Journal of Advances in Modeling Earth Systems
- Adaptive activation functions accelerate convergence in deep and physics-informed neural networks
- (2019) Ameya D. Jagtap et al. JOURNAL OF COMPUTATIONAL PHYSICS
- The relationship between numerical precision and forecast lead time in the Lorenz’95 system
- (2019) Fenwick C. Cooper et al. MONTHLY WEATHER REVIEW
- Machine learning in cardiovascular flows modeling: Predicting arterial blood pressure from non-invasive 4D flow MRI data using physics-informed neural networks
- (2019) Georgios Kissas et al. COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
- Physics-informed neural networks for high-speed flows
- (2019) Zhiping Mao et al. COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
- Could Machine Learning Break the Convection Parameterization Deadlock?
- (2018) P. Gentine et al. GEOPHYSICAL RESEARCH LETTERS
- Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
- (2018) M. Raissi et al. JOURNAL OF COMPUTATIONAL PHYSICS
- Mastering the game of Go without human knowledge
- (2017) David Silver et al. NATURE
- A primal–dual mimetic finite element scheme for the rotating shallow water equations on polygonal spherical meshes
- (2015) John Thuburn et al. JOURNAL OF COMPUTATIONAL PHYSICS
- Deep learning
- (2015) Yann LeCun et al. NATURE
- A guide to RBF-generated finite differences for nonlinear transport: Shallow water simulations on a sphere
- (2012) Natasha Flyer et al. JOURNAL OF COMPUTATIONAL PHYSICS
- Invariant Discretization Schemes for the Shallow-Water Equations
- (2012) Alexander Bihlo et al. SIAM JOURNAL ON SCIENTIFIC COMPUTING
- Horizontal grids for global weather and climate prediction models: a review
- (2011) Andrew Staniforth et al. QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY
Add your recorded webinar
Do you already have a recorded webinar? Grow your audience and get more views by easily listing your recording on Peeref.
Upload NowCreate your own webinar
Interested in hosting your own webinar? Check the schedule and propose your idea to the Peeref Content Team.
Create Now