Filtering by Subject: Computer Sciences
Published: 2019-12-31
Subjects: Applied Mathematics, Computer Sciences, Engineering, Non-linear Dynamics, Numerical Analysis and Computation, Numerical Analysis and Scientific Computing, Partial Differential Equations, Physical Sciences and Mathematics
Numerical modelling frequently involves a diagnostic quantity of interest (QoI) - often of greater importance than the PDE solution - which we seek to accurately approximate. In the case of coastal ocean modelling the power output of a tidal turbine farm is one such example. Goal-oriented error estimation and mesh adaptation can be used to provide meshes which are well-suited to achieving this [...]
Published: 2019-12-04
Subjects: Artificial Intelligence and Robotics, Computer Sciences, Design of Experiments and Sample Surveys, Earth Sciences, Multivariate Analysis, Other Earth Sciences, Physical Sciences and Mathematics, Programming Languages and Compilers, Statistics and Probability
The EOS-compositional reservoir simulation, Design of Experiments, and Proxy Modeling were integrated to obtain the optimal future performance scenario and to construct the most accurate simplified model alternative to the complex reservoir flow simulation. This integrated workflow was adopted on a sector of the main pay/upper sandstone member in the South Rumaila oil field, located in Iraq. [...]
Published: 2019-12-04
Subjects: Computer Sciences, Numerical Analysis and Scientific Computing, Physical Sciences and Mathematics
Non-uniform, dynamically adaptive meshes are a useful tool for reducing computational complexities for geophysical simulations that exhibit strongly localised features such as is the case for example for tsunami, hurricane or typhoon prediction. Theoretical insight for mesh-based numerical methods, however, is largely restricted to uniform meshes as they allow for a traditional definition of [...]
Published: 2019-11-22
Subjects: Computer Sciences, Earth Sciences, Geophysics and Seismology, Numerical Analysis and Scientific Computing, Physical Sciences and Mathematics
Researchers use 2D and 3D spatial models of multivariate data of differing resolutions and formats. It can be challenging to work with multiple datasets, and it is time consuming to set up a robust, performant, grid to handle such spatial models. We share a Python module that provides a framework for containing multidi- mensional data and functionality to work with those data. The module provides [...]
Published: 2019-09-28
Subjects: Computer Sciences, Graphics and Human Computer Interfaces, Physical Sciences and Mathematics
With the development of new sensor technologies and the standardization of sensor data formats, sensor data from different sources becomes available for many applications. A crucial task is to get an overview on the spatial and temporal extent for which data is available before integrating the data into an application. This work presents an approach for accessing the necessary information about [...]
Published: 2019-09-28
Subjects: Computer Sciences, Graphics and Human Computer Interfaces, Physical Sciences and Mathematics
The GEO label is a visual metadata summary that is designed to improve understandability of geospatial metadata. The amount of sensor data collected via and published in Sensor Webs is steadily increasing and thus the published metadata becomes more diverse, more complex and harder to understand. To mitigate this issue, we transfer the GEO label into the Sensor Web architecture in an encompassing [...]
Published: 2019-09-21
Subjects: Applied Mathematics, Computer Sciences, Earth Sciences, Geophysics and Seismology, Numerical Analysis and Computation, Numerical Analysis and Scientific Computing, Physical Sciences and Mathematics, Probability, Statistics and Probability
The mid to lower crust plays an important role in rift initiation and evolution, particularly when large scale sutures and/or terrane boundaries are present. These inherited features can focus strain or act as inhibitors to extensional deformation. Ancient tectonic features are known to exist beneath the iconic failed rift system of the North Sea making it the ideal location to investigate the [...]
Published: 2019-09-19
Subjects: Computer Sciences, Databases and Information Systems, Earth Sciences, Paleontology, Physical Sciences and Mathematics, Stratigraphy
Thirty years ago, the Neptune Database was created to synthesize microfossil occurrences from the deep-sea drilling record. It has been used in numerous studies by both biologists and paleontologists of the evolution and distribution in space and time of marine microplankton. After decades of discontinuous development in various institutions, a significant overhaul of the system was made during [...]
Published: 2019-09-06
Subjects: Computer Sciences, Earth Sciences, Geology, Geophysics and Seismology, Other Computer Sciences, Physical Sciences and Mathematics
Despite the inter-dependence of long term deformation, earthquakes and tsunamis, few modelling approaches bridge these processes. To advance the understanding of tsunami generation and earthquake-tsunami interactions, we present new methods for linking physics-based models of subduction zone geodynamics and seismic cycling, three-dimensional dynamic earthquake rupture, and tsunami generation, [...]
Published: 2019-07-31
Subjects: Artificial Intelligence and Robotics, Atmospheric Sciences, Computational Engineering, Computer Sciences, Engineering, Oceanography and Atmospheric Sciences and Meteorology, Physical Sciences and Mathematics
Numerical weather prediction (NWP) models require ever-growing computing time/resources, but still, have difficulties with predicting weather extremes. Here we introduce a data-driven framework that is based on analog forecasting (prediction using past similar patterns) and employs a novel deep learning pattern-recognition technique (capsule neural networks, CapsNets) and impact-based [...]
Published: 2019-07-04
Subjects: Computer Sciences, Earth Sciences, Environmental Sciences, Geology, Geomorphology, Geophysics and Seismology, Physical Sciences and Mathematics, Probability, Statistics and Probability, Tectonics and Structure
Mapping the distribution of locked segments along subduction megathrusts is essential for improving quantitative assessments of seismic hazard. Previous geodetic studies suggest the Main Himalayan Thrust (MHT) is homogeneously locked (or coupled) along its complete length over a down-dip extent of ~100 km. However, an increasing number of seismological and geophysical observations suggests the [...]
Published: 2019-06-20
Subjects: Applied Mathematics, Artificial Intelligence and Robotics, Atmospheric Sciences, Climate, Computer Sciences, Dynamic Systems, Earth Sciences, Fluid Dynamics, Non-linear Dynamics, Oceanography and Atmospheric Sciences and Meteorology, Physical Sciences and Mathematics, Physics
In this paper, the performance of three deep learning methods for predicting short-term evolution and for reproducing the long-term statistics of a multi-scale spatio-temporal Lorenz 96 system is examined. The methods are: echo state network (a type of reservoir computing, RC-ESN), deep feed-forward artificial neural network (ANN), and recurrent neural network with long short-term memory [...]
Published: 2019-06-19
Subjects: Computer Sciences, Earth Sciences, Physical Sciences and Mathematics
The building blocks of research are developing at an unprecedented pace. Data collection, analysis, interpretation, presentation, review, and publication take place completely on computers. The final product often is still a static document with only limited links to the underlying digital material, making transparency and reproducibility a challenge. In this work we apply the mechanism of badges [...]
Published: 2019-06-18
Subjects: Computer Sciences, Earth Sciences, Geomorphology, Physical Sciences and Mathematics
Automatic and objective extraction of channel networks from topography in systems with multiple interconnected channels, like braided rivers and estuaries, remains a major challenge in hydrology and geomorphology. Representing channelized systems as networks provides a mathematical framework for analyzing transport and geomorphology. In this paper, we introduce a mathematically rigorous [...]
Published: 2019-06-05
Subjects: Artificial Intelligence and Robotics, Atmospheric Sciences, Computer Sciences, Oceanography and Atmospheric Sciences and Meteorology, Physical Sciences and Mathematics
Generative adversarial networks (GANs) are a class of machine learning algorithms with two neural networks, one generator and one discriminator, playing adversarial games with each other. Information maximizing GANs (InfoGANs) is a particular GAN type that tries to maximize mutual information between a subset of latent variables and generated samples, thereby establishing a mapping between the [...]