16 research outputs found
Adaptation of NEMO-LIM3 model for multigrid high resolution Arctic simulation
High-resolution regional hindcasting of ocean and sea ice plays an important
role in the assessment of shipping and operational risks in the Arctic Ocean.
The ice-ocean model NEMO-LIM3 was modified to improve its simulation quality
for appropriate spatio-temporal resolutions. A multigrid model setup with
connected coarse- (14 km) and fine-resolution (5 km) model configurations was
devised. These two configurations were implemented and run separately. The
resulting computational cost was lower when compared to that of the built-in
AGRIF nesting system. Ice and tracer boundary-condition schemes were modified
to achieve the correct interaction between coarse- and fine grids through a
long ice-covered open boundary. An ice-restoring scheme was implemented to
reduce spin-up time. The NEMO-LIM3 configuration described in this article
provides more flexible and customisable tools for high-resolution regional
Arctic simulations
Surrogate-Assisted Evolutionary Generative Design Of Breakwaters Using Deep Convolutional Networks
In the paper, a multi-objective evolutionary surrogate-assisted approach for
the fast and effective generative design of coastal breakwaters is proposed. To
approximate the computationally expensive objective functions, the deep
convolutional neural network is used as a surrogate model. This model allows
optimizing a configuration of breakwaters with a different number of structures
and segments. In addition to the surrogate, an assistant model was developed to
estimate the confidence of predictions. The proposed approach was tested on the
synthetic water area, the SWAN model was used to calculate the wave heights.
The experimental results confirm that the proposed approach allows obtaining
more effective (less expensive with better protective properties) solutions
than non-surrogate approaches for the same time
Generative Design of Physical Objects using Modular Framework
In recent years generative design techniques have become firmly established
in numerous applied fields, especially in engineering. These methods are
demonstrating intensive growth owing to promising outlook. However, existing
approaches are limited by the specificity of problem under consideration. In
addition, they do not provide desired flexibility. In this paper we formulate
general approach to an arbitrary generative design problem and propose novel
framework called GEFEST (Generative Evolution For Encoded STructure) on its
basis. The developed approach is based on three general principles: sampling,
estimation and optimization. This ensures the freedom of method adjustment for
solution of particular generative design problem and therefore enables to
construct the most suitable one. A series of experimental studies was conducted
to confirm the effectiveness of the GEFEST framework. It involved synthetic and
real-world cases (coastal engineering, microfluidics, thermodynamics and oil
field planning). Flexible structure of the GEFEST makes it possible to obtain
the results that surpassing baseline solutions
A Machine Learning Approach for Remote Sensing Data Gap-Filling with Open-Source Implementation: An Example Regarding Land Surface Temperature, Surface Albedo and NDVI
Satellite remote sensing has now become a unique tool for continuous and predictable monitoring of geosystems at various scales, observing the dynamics of different geophysical parameters of the environment. One of the essential problems with most satellite environmental monitoring methods is their sensitivity to atmospheric conditions, in particular cloud cover, which leads to the loss of a significant part of data, especially at high latitudes, potentially reducing the quality of observation time series until it is useless. In this paper, we present a toolbox for filling gaps in remote sensing time-series data based on machine learning algorithms and spatio-temporal statistics. The first implemented procedure allows us to fill gaps based on spatial relationships between pixels, obtained from historical time-series. Then, the second procedure is dedicated to filling the remaining gaps based on the temporal dynamics of each pixel value. The algorithm was tested and verified on Sentinel-3 SLSTR and Terra MODIS land surface temperature data and under different geographical and seasonal conditions. As a result of validation, it was found that in most cases the error did not exceed 1 °C. The algorithm was also verified for gaps restoration in Terra MODIS derived normalized difference vegetation index and land surface broadband albedo datasets. The software implementation is Python-based and distributed under conditions of GNU GPL 3 license via public repository