276 research outputs found
On Transcendental Integral and Transcendental Algebraic Functions and Algebraic Addition Theorems, II
Some Theorems on the Orders of Transcendental Integral Functions of Two Independent Variables
On Transcendental Integral and Transcendental Algebraic Functions and Algebraic Addition Theorems, I
A Theorem Relating to the Orders of a Transcendental Integral Function of Two Independent Variables
Spatio-Temporal Super-Resolution Data Assimilation (SRDA) Utilizing Deep Neural Networks with Domain Generalization
Deep learning has recently gained attention in the atmospheric and oceanic
sciences for its potential to improve the accuracy of numerical simulations or
to reduce computational costs. Super-resolution is one such technique for
high-resolution inference from low-resolution data. This paper proposes a new
scheme, called four-dimensional super-resolution data assimilation (4D-SRDA).
This framework calculates the time evolution of a system from low-resolution
simulations using a physics-based model, while a trained neural network
simultaneously performs data assimilation and spatio-temporal super-resolution.
The use of low-resolution simulations without ensemble members reduces the
computational cost of obtaining inferences at high spatio-temporal resolution.
In 4D-SRDA, physics-based simulations and neural-network inferences are
performed alternately, possibly causing a domain shift, i.e., a statistical
difference between the training and test data, especially in offline training.
Domain shifts can reduce the accuracy of inference. To mitigate this risk, we
developed super-resolution mixup (SR-mixup)--a data augmentation method for
domain generalization. SR-mixup creates a linear combination of randomly
sampled inputs, resulting in synthetic data with a different distribution from
the original data. The proposed methods were validated using an idealized
barotropic ocean jet with supervised learning. The results suggest that the
combination of 4D-SRDA and SR-mixup is effective for robust inference cycles.
This study highlights the potential of super-resolution and
domain-generalization techniques, in the field of data assimilation, especially
for the integration of physics-based and data-driven models
Three-Dimensional Super-Resolution of Passive-Scalar and Velocity Distributions Using Neural Networks for Real-Time Prediction of Urban Micrometeorology
In future cities, micrometeorological predictions will be essential to various services such as drone operations. However, the real-time prediction is difficult even by using a super-computer. To reduce the computation cost, super-resolution (SR) techniques can be utilized, which infer high-resolution images from low-resolution ones. The present paper confirms the validity of three-dimensional (3D) SR for micrometeorology prediction in an urban city. A new neural network is proposed to simultaneously super-resolve 3D temperature and velocity fields. The network is trained using the micrometeorology simulations that incorporate the buildings and 3D radiative transfer. The error of the 3D SR is sufficiently small: 0.14 K for temperature and 0.38 m s-1for velocity. The computation time of the 3D SR is negligible, implying the feasibility of real-time predictions for the urban micrometeorology
Super-Resolution of Three-Dimensional Temperature and Velocity for Building-Resolving Urban Micrometeorology Using Physics-Guided Convolutional Neural Networks with Image Inpainting Techniques
Atmospheric simulations for urban cities can be computationally intensive
because of the need for high spatial resolution, such as a few meters, to
accurately represent buildings and streets. Deep learning has recently gained
attention across various physical sciences for its potential to reduce
computational cost. Super-resolution is one such technique that enhances the
resolution of data. This paper proposes a convolutional neural network (CNN)
that super-resolves instantaneous snapshots of three-dimensional air
temperature and wind velocity fields for urban micrometeorology. This
super-resolution process requires not only an increase in spatial resolution
but also the restoration of missing data caused by the difference in the
building shapes that depend on the resolution. The proposed CNN incorporates
gated convolution, which is an image inpainting technique that infers missing
pixels. The CNN performance has been verified via supervised learning utilizing
building-resolving micrometeorological simulations around Tokyo Station in
Japan. The CNN successfully reconstructed the temperature and velocity fields
around the high-resolution buildings, despite the missing data at lower
altitudes due to the coarseness of the low-resolution buildings. This result
implies that near-surface flows can be inferred from flows above buildings.
This hypothesis was assessed via numerical experiments where all input values
below a certain height were made missing. This research suggests the
possibility that building-resolving micrometeorological simulations become more
practical for urban cities with the aid of neural networks that enhance
computational efficiency
- …