[go: up one dir, main page]

Wang et al., 2024 - Google Patents

A four‐dimensional variational constrained neural network‐based data assimilation method

Wang et al., 2024

View PDF @Full View
Document ID
4958243174035844382
Author
Wang W
Ren K
Duan B
Zhu J
Li X
Ni W
Lu J
Yuan T
Publication year
Publication venue
Journal of Advances in Modeling Earth Systems

External Links

Snippet

Advances in data assimilation (DA) methods and the increasing amount of observations have continuously improved the accuracy of initial fields in numerical weather prediction during the last decades. Meanwhile, in order to effectively utilize the rapidly increasing data …
Continue reading at agupubs.onlinelibrary.wiley.com (PDF) (other versions)

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computer systems based on biological models
    • G06N3/02—Computer systems based on biological models using neural network models
    • G06N3/08—Learning methods
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
    • G06F17/30286—Information retrieval; Database structures therefor; File system structures therefor in structured data stores
    • G06F17/30386—Retrieval requests
    • G06F17/30424—Query processing
    • G06F17/30533—Other types of queries
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
    • G06F17/30286—Information retrieval; Database structures therefor; File system structures therefor in structured data stores
    • G06F17/30587—Details of specialised database models
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10—Complex mathematical operations
    • G06F17/18—Complex mathematical operations for evaluating statistical data, e.g. average values, frequency distributions, probability functions, regression analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00—Subject matter not provided for in other groups of this subclass
    • G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/50—Computer-aided design
    • G06F17/5009—Computer-aided design using simulation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computer systems based on biological models
    • G06N3/02—Computer systems based on biological models using neural network models
    • G06N3/04—Architectures, e.g. interconnection topology
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N7/00—Computer systems based on specific mathematical models
    • G06N7/005—Probabilistic networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computer systems based on biological models
    • G06N3/12—Computer systems based on biological models using genetic models
    • G06N3/126—Genetic algorithms, i.e. information processing using digital simulations of the genetic system
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00—Computer systems utilising knowledge based models
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62—Methods or arrangements for recognition using electronic means
    • G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
    • G06K9/6232—Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods
    • G06K9/6247—Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods based on an approximation criterion, e.g. principal component analysis

Similar Documents

Publication Publication Date Title
Weyn et al. Sub‐seasonal forecasting with a large ensemble of deep‐learning weather prediction models
Wang et al. A four‐dimensional variational constrained neural network‐based data assimilation method
Soma et al. Neural network reconstruction of the dense matter equation of state from neutron star observables
US12112261B2 (en) System and method for model parameter optimization
Mikuni et al. CaloScore v2: single-shot calorimeter shower simulation with diffusion models
Silva et al. Data Assimilation Predictive GAN (DA-PredGAN): applied to determine the spread of COVID-19
Qiao et al. Effective ensemble learning approach for SST field prediction using attention-based PredRNN
Tian et al. Estimation model of global ionospheric irregularities: An artificial intelligence approach
Mousavi et al. Adaptive sequentially space‐filling metamodeling applied in optimal water quantity allocation at basin scale
Dueben et al. Deep learning to improve weather predictions
Krasnopolsky et al. Using machine learning for model physics: An overview
Amemiya et al. Application of recurrent neural networks to model bias correction: Idealized experiments with the Lorenz‐96 model
Lu et al. An efficient bayesian method for advancing the application of deep learning in earth science
Ren et al. Research on satellite orbit prediction based on neural network algorithm
Smith et al. Temporal subsampling diminishes small spatial scales in recurrent neural network emulators of geophysical turbulence
Thavarajah et al. Fast modeling and understanding fluid dynamics systems with encoder–decoder networks
Cao et al. Vicon: Vision in-context operator networks for multi-physics fluid dynamics prediction
Zhong et al. Multi-fidelity enhanced few-shot time series prediction model for structural dynamics analysis
Lang et al. Semi-supervised seismic impedance inversion with convolutional neural network and lightweight transformer
Brahma et al. Visualizing solar irradiance data in ArcGIS and forecasting based on a novel deep neural network mechanism
Solvik et al. 4D‐Var using hessian approximation and backpropagation applied to automatically differentiable numerical and machine learning models
Yarger et al. Autocalibration of the E3SM version 2 atmosphere model using a PCA‐based surrogate for spatial fields
Behrens et al. Simulating atmospheric processes in Earth system models and quantifying uncertainties with deep learning multi‐member and stochastic parameterizations
Yoo et al. Uncertainty-enabled machine learning for emulation of regional sea-level change caused by the Antarctic Ice Sheet
Wang et al. A Four-Dimensional Variational Constrained Neural Network-based Data Assimilation Method