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Computational Science & Discovery is a peer-reviewed journal published by IOP Publishing, covering Magnetic confinement fusion research, Parallel Computing and Optimization Techniques, Protein Structure and Dynamics.
| Publisher | IOP Publishing |
|---|---|
| Reference style | Numbered (IOP) Numbered — [1], [2] in the text [1] Smith A, Jones B and Lee C 2023 A representative article title Computational Science & Discovery 12 45–58
Formats any DOI in Computational Science & Discovery style. No sign-up. |
| Publishes research in | Magnetic confinement fusion research Parallel Computing and Optimization Techniques Protein Structure and Dynamics Advanced Numerical Methods in Computational Mathematics Fluid Dynamics and Turbulent Flows |
| ISSN | 1749-4680 |
| h-index | 26 |
| i10-index | 55 |
| Total citations | 5,803 |
| Top institutions publishing here | Oak Ridge National Laboratory |
| Journal website | iopscience.org |
| You get | A submission-ready PDF and the editable LaTeX source — ready to submit. |
Citations each year’s papers have accumulated so far — the most recent years are still building up.
Sequential model-based optimization (also known as Bayesian optimization) is one of the most efficient methods (per function evaluation) of function minimization. This efficiency makes it appropriate for optimizing the hyperparameters of machine learning algorithms that are slow to train. The Hyperopt library provides algorithms and parallelization infrastructure for performing hyperparameter optimization (model selection) in Python.…
The performance of the open source multiphase flow solver, interFoam, is evaluated in this work. The solver is based on a modified volume of fluid (VoF) approach, which incorporates an interfacial compression flux term to mitigate the effects of numerical smearing of the interface. It forms a part of the C + + libraries and…
Computational science is paramount to the understanding of underlying processes in internal combustion engines of the future that will utilize non-petroleum-based alternative fuels, including carbon-neutral biofuels, and burn in new combustion regimes that will attain high efficiency while minimizing emissions of particulates and nitrogen oxides. Next-generation engines will likely operate at higher pressures, with greater…
We describe RAGE, the ``Radiation Adaptive Grid Eulerian'' radiation-hydrodynamics code, including its data structures, its parallelization strategy and performance, its hydrodynamic algorithm(s), its (gray) radiation diffusion algorithm, and some of the considerable amount of verification and validation efforts. The hydrodynamics is a basic Godunov solver, to which we have made significant improvements to increase the…
