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- Fouilloux, Anne, et al.
(author)
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Building on Communities to Further Software Sustainability
- 2023
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In: Computing in science & engineering (Print). - : IEEE COMPUTER SOC. - 1521-9615 .- 1558-366X. ; 25:3, s. 84-88
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Journal article (peer-reviewed)abstract
- The Nordic e-Infrastructure Collaboration on Earth System Modeling Tools is a small community comprising members with diverse backgrounds, skills, and interests. Largely dependent on temporary staff to develop, operate, and maintain large scientific codes, this community devised strategies to enhance software reusability and sustainability. These strategies include collaborating with other communities for support, adopting Open Science as well as findable, accessible, interoperable, and reusable principles to optimize resource usage, growing essential knowledge within the community, and setting up a community of practice to facilitate onboarding and offboarding. The strategies also promote inclusiveness, foster external collaboration, and recognize technical contributions.
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- Kung, Rebecca, et al.
(author)
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Improving students' self-assessment of numerical analysis projects.
- 2007
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In: Computing in Science and Engineering. - 1521-9615. ; 9:4, s. 92-95
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Journal article (peer-reviewed)abstract
- Students need self-assessment skills, yet in our experience, these skills appear to be severely lacking in undergraduate students. Descriptions from student interviews and related education research results suggest ways to increase students' ability to self-assess.
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- Lapenta, Giovanni, et al.
(author)
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Space Weather Prediction and Exascale Computing
- 2013
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In: Computing in science & engineering (Print). - 1521-9615 .- 1558-366X. ; 15:5, s. 68-76
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Journal article (peer-reviewed)abstract
- Space weather can have a great effect on Earth's climate. Predicting the impact of space environment disturbances on Earth presents a challenge to scientists. Here, the ExaScience Lab's efforts are presented, which use exascale computing and new visualization tools to predict the arrival and impact of space events on Earth.
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- Liu, Ming, et al.
(author)
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A High-End Reconfigurable Computation Platform for Nuclear and Particle Physics Experiments
- 2011
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In: Computing in science & engineering (Print). - 1521-9615 .- 1558-366X. ; 13:2, s. 52-63
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Journal article (peer-reviewed)abstract
- A high-performance computation platform based on field-programmable gate arrays targets nuclear and particle physics experiment applications. The system can be constructed or scaled into a supercomputer-equivalent size for detector data processing by inserting compute nodes into advanced telecommunications computing architecture (ATCA) crates. Among the case study results are that one ATCA crate can provide a computation capability equivalent to hundreds of commodity PCs for Hades online particle track reconstruction and Cherenkov ring recognition.
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- Sletholt, Magnus Thorstein, et al.
(author)
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What Do We Know about Scientific Software Development's Agile Practices?
- 2012
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In: Computing in Science & Engineering. - 1521-9615. ; 14:2, s. 24-36
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Journal article (peer-reviewed)abstract
- The development of scientific software has similarities with processes that follow the software engineering "agile manifesto": responsiveness to change and collaboration are of utmost importance. But how well do current scientific software-development processes match the practices found in agile development methods, and what are the effects of using agile practices in such processes?
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- Strömbäck, Lena, et al.
(author)
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XML Management for Bioinformatics Applications
- 2011
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In: Computing in science & engineering (Print). - : Institute of Electrical and Electronics Engineers (IEEE). - 1521-9615 .- 1558-366X. ; 13:5, s. 12-22
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Journal article (peer-reviewed)abstract
- Scientific exploration has become a data-intensive process, and increasing amounts of data need to be stored, analyzed, and shared. XML can help address these needs. As concrete examples from systems biology show, native XML storage can be combined with traditional relational databases to offer an effective, usable solution for storing scientific data.
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10. |
- Vinuesa, Ricardo, et al.
(author)
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Emerging Trends in Machine Learning for Computational Fluid Dynamics
- 2022
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In: Computing in science & engineering (Print). - : Institute of Electrical and Electronics Engineers (IEEE). - 1521-9615 .- 1558-366X. ; 24:5, s. 33-41
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Journal article (peer-reviewed)abstract
- The renewed interest from the scientific community in machine learning (ML) is opening many new areas of research. Here we focus on trends in ML that are providing opportunities to advance the field of computational fluid dynamics (CFD). We discuss synergies between ML and CFD that have already shown benefits, and we also assess areas that are under development and may produce important benefits in the coming years. We believe that it is also important to emphasize a balanced perspective of cautious optimism for these emerging approaches.
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