213 citations · 730 across the 12 of their papers we have counts for
12 papers
Enabling Dynamic and Intelligent Workflows for HPC, Data Analytics, and AI Convergence
Jorge Ejarque, Rosa M. Badia, Loïc Albertin +34
The evolution of High-Performance Computing (HPC) platforms enables the design and execution of progressively larger and more complex workflow applications in these systems. The co…
Intra-domain and cross-domain transfer learning for time series data -- How transferable are the features?
Erik Otović, Marko Njirjak, Dario Jozinović +3
In practice, it is very demanding and sometimes impossible to collect datasets of tagged data large enough to successfully train a machine learning model, and one possible solution…
Graph Neural Networks for Multivariate Time Series Regression with Application to Seismic Data
Stefan Bloemheuvel, Jurgen van den Hoogen, Dario Jozinović +2
Machine learning, with its advances in deep learning has shown great potential in analyzing time series. In many scenarios, however, additional information that can potentially imp…
SeisBench -- A Toolbox for Machine Learning in Seismology
Jack Woollam, Jannes Münchmeyer, Frederik Tilmann +10
Machine Learning (ML) methods have seen widespread adoption in seismology in recent years. The ability of these techniques to efficiently infer the statistical properties of large…
Which picker fits my data? A quantitative evaluation of deep learning based seismic pickers
Jannes Münchmeyer, Jack Woollam, Andreas Rietbrock +10
Seismic event detection and phase picking are the base of many seismological workflows. In recent years, several publications demonstrated that deep learning approaches significant…
Transfer learning: Improving neural network based prediction of earthquake ground shaking for an area with insufficient training data
Dario Jozinović, Anthony Lomax, Ivan Štajduhar +1
In a recent study (Jozinović et al, 2020) we showed that convolutional neural networks (CNNs) applied to network seismic traces can be used for rapid prediction of earthquake peak…
Local earthquakes detection: A benchmark dataset of 3-component seismograms built on a global scale
Fabrizio Magrini, Dario Jozinović, Fabio Cammarano +2
Machine learning is becoming increasingly important in scientific and technological progress, due to its ability to create models that describe complex data and generalize well. Th…
Rapid Prediction of Earthquake Ground Shaking Intensity Using Raw Waveform Data and a Convolutional Neural Network
Dario Jozinović, Anthony Lomax, Ivan Štajduhar +1
This study describes a deep convolutional neural network (CNN) based technique for the prediction of intensity measurements (IMs) of ground shaking. The input data to the CNN model…
VERCE delivers a productive e-Science environment for seismology research
Malcolm Atkinson, Michele Carpené, Emanuele Casarotti +18
The VERCE project has pioneered an e-Infrastructure to support researchers using established simulation codes on high-performance computers in conjunction with multiple sources of…
Non-Abelian monopole-vortex complex
Mattia Cipriani, Daniele Dorigoni, Sven Bjarke Gudnason +2
In the context of softly broken N=2 supersymmetric quantum chromodynamics (SQCD), with a hierarchical gauge symmetry breaking SU(N+1) -> U(N) -> 1, at scales v1 and v2, respectivel…
Nonabelian Faddeev-Niemi Decomposition of the SU(3) Yang-Mills Theory
Jarah Evslin, Simone Giacomelli, Kenichi Konishi +1
Faddeev and Niemi (FN) have introduced an abelian gauge theory which simulates dynamical abelianization in Yang-Mills theory (YM). It contains both YM instantons and Wu-Yang monopo…
Monopole-vortex complex in a theta vacuum
Kenichi Konishi, Alberto Michelini, Keisuke Ohashi
We discuss aspects of the monopole-vortex complex soliton arising in a hierarchically broken gauge system, G to H to 1, in a theta vacuum of the underlying G theory. Here we focus…