Related references
Note: Only part of the references are listed.A Review on Generative Adversarial Networks: Algorithms, Theory, and Applications
Jie Gui et al.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING (2023)
Integrating Scientific Knowledge with Machine Learning for Engineering and Environmental Systems
Jared Willard et al.
ACM COMPUTING SURVEYS (2023)
A physics-informed diffusion model for high-fidelity flow field reconstruction
Dule Shu et al.
JOURNAL OF COMPUTATIONAL PHYSICS (2023)
Taming Lagrangian chaos with multi-objective reinforcement learning
Chiara Calascibetta et al.
EUROPEAN PHYSICAL JOURNAL E (2023)
Reconstructing Rayleigh-Benard flows out of temperature-only measurements using Physics-Informed Neural Networks
Patricio Clark Di Leoni et al.
EUROPEAN PHYSICAL JOURNAL E (2023)
Curriculum learning for data-driven modeling of dynamical systems
Michele Alessandro Bucci et al.
EUROPEAN PHYSICAL JOURNAL E (2023)
Deep reinforcement learning for the olfactory search POMDP: a quantitative benchmark
Aurore Loisy et al.
EUROPEAN PHYSICAL JOURNAL E (2023)
A deep-learning approach for reconstructing 3D turbulent flows from 2D observation data
Mustafa Z. Yousif et al.
Scientific Reports (2023)
Machine learning and numerical investigation on drag reduction of underwater serial multi-projectiles
Xi Huang et al.
DEFENCE TECHNOLOGY (2022)
Super-Resolving Ocean Dynamics from Space with Computer Vision Algorithms
Bruno Buongiorno Nardelli et al.
REMOTE SENSING (2022)
Inferring turbulent environments via machine learning
Michele Buzzicotti et al.
EUROPEAN PHYSICAL JOURNAL E (2022)
A Survey of Convolutional Neural Networks: Analysis, Applications, and Prospects
Zewen Li et al.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2022)
Machine-Learning-Based Reconstruction of Turbulent Vortices From Sparse Pressure Sensors in a Pump Sump
Kai Fukami et al.
JOURNAL OF FLUIDS ENGINEERING-TRANSACTIONS OF THE ASME (2022)
Searching for a source without gradients: how good is infotaxis and how to beat it
Aurore Loisy et al.
PROCEEDINGS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES (2022)
Towards extraction of orthogonal and parsimonious non-linear modes from turbulent flows
Hamidreza Eivazi et al.
EXPERT SYSTEMS WITH APPLICATIONS (2022)
Deep Generative Modelling: A Comparative Review of VAEs, GANs, Normalizing Flows, Energy-Based and Autoregressive Models
Sam Bond-Taylor et al.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE (2022)
Global energy spectrum of the general oceanic circulation
Benjamin A. Storer et al.
NATURE COMMUNICATIONS (2022)
Enhancing computational fluid dynamics with machine learning
Ricardo Vinuesa et al.
NATURE COMPUTATIONAL SCIENCE (2022)
Reconstruction by inpainting for visual anomaly detection
Vitjan Zavrtanik et al.
PATTERN RECOGNITION (2021)
Machine-learning-based spatio-temporal super resolution reconstruction of turbulent flows
Kai Fukami et al.
JOURNAL OF FLUID MECHANICS (2021)
Unsupervised deep learning for super-resolution reconstruction of turbulence
Hyojin Kim et al.
JOURNAL OF FLUID MECHANICS (2021)
A review of uncertainty quantification in deep learning: Techniques, applications and challenges
Moloud Abdar et al.
INFORMATION FUSION (2021)
Convolutional-network models to predict wall-bounded turbulence from wall quantities
Luca Guastoni et al.
JOURNAL OF FLUID MECHANICS (2021)
Aleatoric and epistemic uncertainty in machine learning: an introduction to concepts and methods
Eyke Huellermeier et al.
MACHINE LEARNING (2021)
Physics-informed machine learning: case studies for weather and climate modelling
K. Kashinath et al.
PHILOSOPHICAL TRANSACTIONS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES (2021)
Combining data assimilation and machine learning to infer unresolved scale parametrization
Julien Brajard et al.
PHILOSOPHICAL TRANSACTIONS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES (2021)
Can deep learning beat numerical weather prediction?
M. G. Schultz et al.
PHILOSOPHICAL TRANSACTIONS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES (2021)
Convolutional neural network and long short-term memory based reduced order surrogate for minimal turbulent channel flow
Taichi Nakamura et al.
PHYSICS OF FLUIDS (2021)
Deep learning velocity signals allow quantifying turbulence intensity
Alessandro Corbetta et al.
SCIENCE ADVANCES (2021)
Highly accurate protein structure prediction with AlphaFold
John Jumper et al.
NATURE (2021)
From coarse wall measurements to turbulent velocity fields through deep learning
A. Guemes et al.
PHYSICS OF FLUIDS (2021)
Data-Driven Mapping With Prediction Neural Network for the Future Wide-Swath Satellite Altimetry
Jiankai Di et al.
FRONTIERS IN MARINE SCIENCE (2021)
Physics-informed machine learning
George Em Karniadakis et al.
NATURE REVIEWS PHYSICS (2021)
Machine Learning for Fluid Mechanics
Steven L. Brunton et al.
ANNUAL REVIEW OF FLUID MECHANICS, VOL 52 (2020)
Upper-ocean Ekman current dynamics: a new perspective
Victor Shrira et al.
JOURNAL OF FLUID MECHANICS (2020)
Statistical Properties of Turbulence in the Presence of a Smart Small-Scale Control
Michele Buzzicotti et al.
PHYSICAL REVIEW LETTERS (2020)
Deep learning for irregularly and regularly missing data reconstruction
Xintao Chai et al.
SCIENTIFIC REPORTS (2020)
Active flow control using machine learning: A brief review
Feng Ren et al.
JOURNAL OF HYDRODYNAMICS (2020)
Machine-learning-based feedback control for drag reduction in a turbulent channel flow
Jonghwan Park et al.
JOURNAL OF FLUID MECHANICS (2020)
Spatio-temporal deep learning models of 3D turbulence with physics informed diagnostics
Arvind T. Mohan et al.
JOURNAL OF TURBULENCE (2020)
Comparison of Cloud-Filling Algorithms for Marine Satellite Data
Andy Stock et al.
REMOTE SENSING (2020)
Synchronizing subgrid scale models of turbulence to data
Michele Buzzicotti et al.
PHYSICS OF FLUIDS (2020)
An Artificial Neural Network to Infer the Mediterranean 3D Chlorophyll-a and Temperature Fields from Remote Sensing Observations
Michela Sammartino et al.
REMOTE SENSING (2020)
Deep learning for tomographic image reconstruction
Ge Wang et al.
NATURE MACHINE INTELLIGENCE (2020)
Machine learning strategies for path-planning microswimmers in turbulent flows
Jaya Kumar Alageshan et al.
PHYSICAL REVIEW E (2020)
DINCAE 1.0: a convolutional neural network with error estimates to reconstruct sea surface temperature satellite observations
Alexander Barth et al.
GEOSCIENTIFIC MODEL DEVELOPMENT (2020)
Deep learning for intensity mapping observations: component extraction
Kana Moriwaki et al.
MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY (2020)
Turbulence Modeling in the Age of Data
Karthik Duraisamy et al.
ANNUAL REVIEW OF FLUID MECHANICS, VOL 51 (2019)
Deep-learning-based seismic data interpolation: A preliminary result
Benfeng Wang et al.
GEOPHYSICS (2019)
Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
M. Raissi et al.
JOURNAL OF COMPUTATIONAL PHYSICS (2019)
Deep learning and process understanding for data-driven Earth system science
Markus Reichstein et al.
NATURE (2019)
A gentle introduction to deep learning in medical image processing
Andreas Maier et al.
ZEITSCHRIFT FUR MEDIZINISCHE PHYSIK (2019)
Applications, promises, and pitfalls of deep learning for fluorescence image reconstruction
Chinmay Belthangady et al.
NATURE METHODS (2019)
Self-Similar Subgrid-Scale Models for Inertial Range Turbulence and Accurate Measurements of Intermittency
Luca Biferale et al.
PHYSICAL REVIEW LETTERS (2019)
Reconstruction of Ocean Color Data Using Machine Learning Techniques in Polar Regions: Focusing on Off Cape Hallett, Ross Sea
Jinku Park et al.
REMOTE SENSING (2019)
DeepCMB: Lensing reconstruction of the cosmic microwave background with deep neural networks
J. Caldeira et al.
ASTRONOMY AND COMPUTING (2019)
Zermelo's problem: Optimal point-to-point navigation in 2D turbulent flows using reinforcement learning
L. Biferale et al.
CHAOS (2019)
Control of chaotic systems by deep reinforcement learning
M. A. Bucci et al.
PROCEEDINGS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES (2019)
Applications of Deep Learning to Ocean Data Inference and Subgrid Parameterization
Thomas Bolton et al.
JOURNAL OF ADVANCES IN MODELING EARTH SYSTEMS (2019)
On the resolutions of ocean altimetry maps
Maxime Ballarotta et al.
OCEAN SCIENCE (2019)
Integrating machine learning and multiscale modeling-perspectives, challenges, and opportunities in the biologica biomedical, and behavioral sciences
Mark Alber et al.
NPJ DIGITAL MEDICINE (2019)
Systematically Understanding the Cyber Attack Business: A Survey
Keman Huang et al.
ACM COMPUTING SURVEYS (2018)
Image Reconstruction Is a New Frontier of Machine Learning
Ge Wang et al.
IEEE TRANSACTIONS ON MEDICAL IMAGING (2018)
Effect of filter type on the statistics of energy transfer between resolved and subfilter scales from a-priori analysis of direct numerical simulations of isotropic turbulence
M. Buzzicotti et al.
JOURNAL OF TURBULENCE (2018)
Efficient collective swimming by harnessing vortices through deep reinforcement learning
Siddhartha Verma et al.
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA (2018)
Data assimilation in the geosciences: An overview of methods, issues, and perspectives
Alberto Carrassi et al.
WILEY INTERDISCIPLINARY REVIEWS-CLIMATE CHANGE (2018)
Glider soaring via reinforcement learning in the field
Gautam Reddy et al.
NATURE (2018)
Generative Adversarial Networks: Introduction and Outlook
Kunfeng Wang et al.
IEEE-CAA JOURNAL OF AUTOMATICA SINICA (2017)
DUACS DT2014: the new multi-mission altimeter data set reprocessed over 20 years
Marie-Isabelle Pujol et al.
OCEAN SCIENCE (2016)
Numerical simulation of real-world flows
Toshiyuki Hayase
FLUID DYNAMICS RESEARCH (2015)
Gappy data: To krig or not to krig?
H Gunes et al.
JOURNAL OF COMPUTATIONAL PHYSICS (2006)
Spatio-temporal filling of missing points in geophysical data sets
D. Kondrashov et al.
NONLINEAR PROCESSES IN GEOPHYSICS (2006)
Extended proper orthogonal decomposition: Application to jet/vortex interaction
S Maurel et al.
FLOW TURBULENCE AND COMBUSTION (2001)