Artificial Neural Networks emulating Representer Method at a shallow water model 2D

Furtado, Helaine C. M.; Cintra, Rosangela; Velho, Haroldo de Campos

doi:10.5540/03.2016.004.01.0097

Cited by 3 publications

(3 citation statements)

References 6 publications

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“…The machine learning methods for DA are summarized in Table 5. Wave model [111] MLP PF Lorenz model [120] MLP KF Three-wave model [121] MLP Variational Lorenz model [122] MLP Variational Wave model [107] Elman KF Shallow water 1D model (DYNAMO-1D) [104] RBF KF Shallow water 1D model (DYNAMO-1D) [112] MLP LETKF Atmospheric general circulation model (FSUGSM) [123] MLP LETKF Atmospheric general circulation model (SPEEDY) [124] Mixed Type KF Satellite-Derived Sea Surface Temperature data [125] Fully Connected Variational , KF Dot system and Lorenz models [117] LSTM Variational (3DVAR) CFD model (Fluidity) [114] Elman Variational Dot system and Lorenz models [102] LSTM KF CFD model (Fluidity) [101] MLP Variational Lorenz model [126] LSTM Variational Lorenz model [127] MLP Variational and EnKF Lorenz model [118] LSTM KF Oxygen diffusion across the Blood-Brain Barrier model…”

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confidence: 99%

Data Assimilation for Agent-Based Models

Ghorbani,

Nazari-Heris

et al. 2023

Mathematics

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This article presents a comprehensive review of the existing literature on the topic of data assimilation for agent-based models, with a specific emphasis on pedestrians and passengers within the context of transportation systems. This work highlights a plethora of advanced techniques that may have not been previously employed for online pedestrian simulation, and may therefore offer significant value to readers in this domain. Notably, these methods often necessitate a sophisticated understanding of mathematical principles such as linear algebra, probability theory, singular value decomposition, optimization, machine learning, and compressed sensing. Despite this complexity, this article strives to provide a nuanced explanation of these mathematical underpinnings. It is important to acknowledge that the subject matter under study is still in its nascent stages, and as such, it is highly probable that new techniques will emerge in the coming years. One potential avenue for future exploration involves the integration of machine learning with Agent-based Data Assimilation (ABDA, i.e., data assimilation methods used for agent-based models) methods.

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confidence: 99%

Data Assimilation for Agent-Based Models

Ghorbani,

Nazari-Heris

et al. 2023

Mathematics

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“…Como aponta a cartela da Amérique méridionale, 3 o viajante francês Charles Marie de La Condamine desempenhou importante papel na configuração espacial que D'Anville imprimiu à região da bacia Amazônica, mesmo em trechos, como o rio Negro, que jamais visitou. 4 La Condamine, juntamente com Pierre Bouguer, sob a direção de Louis Godin, integrou, em 1735, a expedição espano-francesa que foi ao Peru medir um arco de meridiano, no contexto do debate sobre o formato da terra. 5 Ao fim da expedição, ele decidiu retornar à Europa descendo o Amazonas, o que fez entre maio e setembro de 1743, pois o trajeto do rio era de grande interesse entre os savants europeus, já que seu conhecimento era, até então, bastante relativo.…”

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O paraíso amazônico e seus mitos cartográficos

Furtado¹

2020

terrabrasilis

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“…[13] applied NN to emulate the particle filter and the variational data assimilation (4D var) for the Lorenz chaotic system. In 2012, Furtado [40] used an ocean model to emulate a variational method called representer. The NN technique was successful for all experiments, but they use theoretical or low-dimensional models.…”

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Data Assimilation by Artificial Neural Networks for an Atmospheric General Circulation Model

Cintra¹,

Velho²

2018

Advanced Applications for Artificial Neural Networks

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Numerical weather prediction (NWP) uses atmospheric general circulation models (AGCMs) to predict weather based on current weather conditions. The process of entering observation data into mathematical model to generate the accurate initial conditions is called data assimilation (DA). It combines observations, forecasting, and filtering step. This paper presents an approach for employing artificial neural networks (NNs) to emulate the local ensemble transform Kalman filter (LETKF) as a method of data assimilation. This assimilation experiment tests the Simplified Parameterizations PrimitivE-Equation Dynamics (SPEEDY) model, an atmospheric general circulation model (AGCM), using synthetic observational data simulating localizations of meteorological balloons. For the data assimilation scheme, the supervised NN, the multilayer perceptrons (MLPs) networks are applied. After the training process, the method, forehead-calling MLP-DA, is seen as a function of data assimilation. The NNs were trained with data from first 3 months of 1982, 1983, and 1984. The experiment is performed for January 1985, one data assimilation cycle using MLP-DA with synthetic observations. The numerical results demonstrate the effectiveness of the NN technique for atmospheric data assimilation. The results of the NN analyses are very close to the results from the LETKF analyses, the differences of the monthly average of absolute temperature analyses are of order 10-2. The simulations show that the major advantage of using the MLP-DA is better computational performance, since the analyses have similar quality. The CPU-time cycle assimilation with MLP-DA analyses is 90 times faster than LETKF cycle assimilation with the mean analyses used to run the forecast experiment.

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Artificial Neural Networks emulating Representer Method at a shallow water model 2D

Cited by 3 publications

References 6 publications

Data Assimilation for Agent-Based Models

Data Assimilation for Agent-Based Models

O paraíso amazônico e seus mitos cartográficos

Data Assimilation by Artificial Neural Networks for an Atmospheric General Circulation Model

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