Data assimilation empowered neural network parametrizations for subgrid processes in geophysical flows
Published 2021 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
Data assimilation empowered neural network parametrizations for subgrid processes in geophysical flows
Authors
Keywords
-
Journal
Physical Review Fluids
Volume 6, Issue 5, Pages -
Publisher
American Physical Society (APS)
Online
2021-05-12
DOI
10.1103/physrevfluids.6.050501
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- Lift & Learn: Physics-informed machine learning for large-scale nonlinear dynamical systems
- (2020) Elizabeth Qian et al. PHYSICA D-NONLINEAR PHENOMENA
- A Maximum Likelihood Ensemble Filter via a Modified Cholesky Decomposition for Non-Gaussian Data Assimilation
- (2020) Elias David Nino-Ruiz et al. SENSORS
- A priori analysis on deep learning of subgrid-scale parameterizations for Kraichnan turbulence
- (2020) Suraj Pawar et al. THEORETICAL AND COMPUTATIONAL FLUID DYNAMICS
- Combining machine learning with knowledge-based modeling for scalable forecasting and subgrid-scale closure of large, complex, spatiotemporal systems
- (2020) Alexander Wikner et al. CHAOS
- A Machine Learning‐Based Global Atmospheric Forecast Model
- (2020) Troy Arcomano et al. GEOPHYSICAL RESEARCH LETTERS
- Harmonizing models and observations: Data assimilation in Earth system science
- (2020) Xin Li et al. Science China-Earth Sciences
- Modeling subgrid-scale forces by spatial artificial neural networks in large eddy simulation of turbulence
- (2020) Chenyue Xie et al. Physical Review Fluids
- Data-driven modelling of nonlinear spatio-temporal fluid flows using a deep convolutional generative adversarial network
- (2020) M. Cheng et al. COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
- A deep-learning-based surrogate model for data assimilation in dynamic subsurface flow problems
- (2020) Meng Tang et al. JOURNAL OF COMPUTATIONAL PHYSICS
- Backpropagation algorithms and Reservoir Computing in Recurrent Neural Networks for the forecasting of complex spatiotemporal dynamics
- (2020) P.R. Vlachas et al. NEURAL NETWORKS
- Shallow neural networks for fluid flow reconstruction with limited sensors
- (2020) N. Benjamin Erichson et al. PROCEEDINGS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES
- Combining data assimilation and machine learning to emulate a dynamical model from sparse and noisy observations: A case study with the Lorenz 96 model
- (2020) Julien Brajard et al. Journal of Computational Science
- A turbulent eddy-viscosity surrogate modeling framework for Reynolds-averaged Navier-Stokes simulations
- (2020) Romit Maulik et al. COMPUTERS & FLUIDS
- Predictive large-eddy-simulation wall modeling via physics-informed neural networks
- (2019) X. I. A. Yang et al. Physical Review Fluids
- Deep learning and process understanding for data-driven Earth system science
- (2019) Markus Reichstein et al. NATURE
- Symmetries in the Lorenz-96 Model
- (2019) Dirk L. van Kekem et al. INTERNATIONAL JOURNAL OF BIFURCATION AND CHAOS
- Reynolds-averaged Navier–Stokes equations with explicit data-driven Reynolds stress closure can be ill-conditioned
- (2019) Jinlong Wu et al. JOURNAL OF FLUID MECHANICS
- Super-resolution reconstruction of turbulent flows with machine learning
- (2019) Kai Fukami et al. JOURNAL OF FLUID MECHANICS
- Assessing the scales in numerical weather and climate predictions: will exascale be the rescue?
- (2019) Philipp Neumann et al. PHILOSOPHICAL TRANSACTIONS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES
- Predictions of turbulent shear flows using deep neural networks
- (2019) P. A. Srinivasan et al. Physical Review Fluids
- Spatially Extended Tests of a Neural Network Parametrization Trained by Coarse‐graining
- (2019) Noah D. Brenowitz et al. Journal of Advances in Modeling Earth Systems
- Machine Learning for Fluid Mechanics
- (2019) Steven L. Brunton et al. Annual Review of Fluid Mechanics
- A deep learning enabler for nonintrusive reduced order modeling of fluid flows
- (2019) S. Pawar et al. PHYSICS OF FLUIDS
- Prediction of turbulent heat transfer using convolutional neural networks
- (2019) Junhyuk Kim et al. JOURNAL OF FLUID MECHANICS
- Perspective on machine learning for advancing fluid mechanics
- (2019) M. P. Brenner et al. Physical Review Fluids
- Continuous data assimilation reduced order models of fluid flow
- (2019) Camille Zerfas et al. COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
- Ensemble Kalman Filter for Assimilating Experimental Data into Large-Eddy Simulations of Turbulent Flows
- (2019) Jeffrey W. Labahn et al. FLOW TURBULENCE AND COMBUSTION
- Deep neural networks for data-driven LES closure models
- (2019) Andrea Beck et al. JOURNAL OF COMPUTATIONAL PHYSICS
- Model reduction of dynamical systems on nonlinear manifolds using deep convolutional autoencoders
- (2019) Kookjin Lee et al. JOURNAL OF COMPUTATIONAL PHYSICS
- Deep learning for physical processes: incorporating prior scientific knowledge
- (2019) Emmanuel de Bézenac et al. JOURNAL OF STATISTICAL MECHANICS-THEORY AND EXPERIMENT
- Enforcing statistical constraints in generative adversarial networks for modeling chaotic dynamical systems
- (2019) Jin-Long Wu et al. JOURNAL OF COMPUTATIONAL PHYSICS
- Ensemble-Based State Estimator for Aerodynamic Flows
- (2018) Andre F. C. da Silva et al. AIAA JOURNAL
- Could Machine Learning Break the Convection Parameterization Deadlock?
- (2018) P. Gentine et al. GEOPHYSICAL RESEARCH LETTERS
- Prognostic Validation of a Neural Network Unified Physics Parameterization
- (2018) N. D. Brenowitz et al. GEOPHYSICAL RESEARCH LETTERS
- Hidden physics models: Machine learning of nonlinear partial differential equations
- (2018) Maziar Raissi et al. JOURNAL OF COMPUTATIONAL PHYSICS
- Model-Free Prediction of Large Spatiotemporally Chaotic Systems from Data: A Reservoir Computing Approach
- (2018) Jaideep Pathak et al. PHYSICAL REVIEW LETTERS
- Data-assisted reduced-order modeling of extreme events in complex dynamical systems
- (2018) Zhong Yi Wan et al. PLoS One
- Turbulence Modeling in the Age of Data
- (2018) Karthik Duraisamy et al. Annual Review of Fluid Mechanics
- Deep learning to represent subgrid processes in climate models
- (2018) Stephan Rasp et al. PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
- Parameterised non-intrusive reduced order methods for ensemble Kalman filter data assimilation
- (2018) D. Xiao et al. COMPUTERS & FLUIDS
- Subgrid modelling for two-dimensional turbulence using neural networks
- (2018) R. Maulik et al. JOURNAL OF FLUID MECHANICS
- Optimal reduced space for Variational Data Assimilation
- (2018) Rossella Arcucci et al. JOURNAL OF COMPUTATIONAL PHYSICS
- 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
- Middle atmosphere dynamical sources of the semiannual oscillation in the thermosphere and ionosphere
- (2017) M. Jones et al. GEOPHYSICAL RESEARCH LETTERS
- Theory-Guided Data Science: A New Paradigm for Scientific Discovery from Data
- (2017) Anuj Karpatne et al. IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
- The Analog Data Assimilation
- (2017) Redouane Lguensat et al. MONTHLY WEATHER REVIEW
- A proof of concept for scale-adaptive parametrizations: the case of the Lorenz '96 model
- (2017) Gabriele Vissio et al. QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY
- Searching for turbulence models by artificial neural network
- (2017) Masataka Gamahara et al. Physical Review Fluids
- Physics-informed machine learning approach for reconstructing Reynolds stress modeling discrepancies based on DNS data
- (2017) Jian-Xun Wang et al. Physical Review Fluids
- Uncertainty in Model Climate Sensitivity Traced to Representations of Cumulus Precipitation Microphysics
- (2016) Ming Zhao et al. JOURNAL OF CLIMATE
- Reconstruction of unsteady viscous flows using data assimilation schemes
- (2016) V. Mons et al. JOURNAL OF COMPUTATIONAL PHYSICS
- Reynolds averaged turbulence modelling using deep neural networks with embedded invariance
- (2016) Julia Ling et al. JOURNAL OF FLUID MECHANICS
- Review of the Ensemble Kalman Filter for Atmospheric Data Assimilation
- (2016) P. L. Houtekamer et al. MONTHLY WEATHER REVIEW
- Filter accuracy for the Lorenz 96 model: Fixed versus adaptive observation operators
- (2016) K.J.H. Law et al. PHYSICA D-NONLINEAR PHENOMENA
- Theory-Guided Machine Learning in Materials Science
- (2016) Nicholas Wagner et al. Frontiers in Materials
- Deep learning
- (2015) Yann LeCun et al. NATURE
- Theory-Guided Data Science for Climate Change
- (2014) James H. Faghmous et al. COMPUTER
- Earth observation satellite sensors for biodiversity monitoring: potentials and bottlenecks
- (2014) Claudia Kuenzer et al. INTERNATIONAL JOURNAL OF REMOTE SENSING
- Approximate deconvolution large eddy simulation of a stratified two-layer quasigeostrophic ocean model
- (2013) Omer San et al. OCEAN MODELLING
- High-order methods for decaying two-dimensional homogeneous isotropic turbulence
- (2012) Omer San et al. COMPUTERS & FLUIDS
- Subgrid modelling for geophysical flows
- (2012) J. S. Frederiksen et al. PHILOSOPHICAL TRANSACTIONS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES
- Statistical mechanics of two-dimensional and geophysical flows
- (2012) Freddy Bouchet et al. PHYSICS REPORTS-REVIEW SECTION OF PHYSICS LETTERS
- Two-Dimensional Turbulence
- (2011) Guido Boffetta et al. Annual Review of Fluid Mechanics
- State estimation in wall-bounded flow systems. Part 3. The ensemble Kalman filter
- (2011) C. H. COLBURN et al. JOURNAL OF FLUID MECHANICS
- The role of large-scale spatial patterns in the chaotic amplification of perturbations in a Lorenz’96 model
- (2011) S. Herrera et al. TELLUS SERIES A-DYNAMIC METEOROLOGY AND OCEANOGRAPHY
- Accelerating Progress in Global Atmospheric Model Development through Improved Parameterizations
- (2010) Christian Jakob BULLETIN OF THE AMERICAN METEOROLOGICAL SOCIETY
- Extensive chaos in the Lorenz-96 model
- (2010) A. Karimi et al. CHAOS
- A Non-Gaussian Ensemble Filter Update for Data Assimilation
- (2010) Jeffrey L. Anderson MONTHLY WEATHER REVIEW
- The maximum likelihood ensemble filter performances in chaotic systems
- (2009) Alberto Carrassi et al. TELLUS SERIES A-DYNAMIC METEOROLOGY AND OCEANOGRAPHY
- Subgrid-Scale Parameterization with Conditional Markov Chains
- (2008) Daan Crommelin et al. JOURNAL OF THE ATMOSPHERIC SCIENCES
- Moisture Vertical Structure, Column Water Vapor, and Tropical Deep Convection
- (2008) Christopher E. Holloway et al. JOURNAL OF THE ATMOSPHERIC SCIENCES
- A deterministic formulation of the ensemble Kalman filter: an alternative to ensemble square root filters
- (2008) Pavel Sakov et al. TELLUS SERIES A-DYNAMIC METEOROLOGY AND OCEANOGRAPHY
- Towards a mesoscale eddy closure
- (2007) Carsten Eden et al. OCEAN MODELLING
Find Funding. Review Successful Grants.
Explore over 25,000 new funding opportunities and over 6,000,000 successful grants.
ExploreAdd 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 Now