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Author: Eric Moge  or Audun Skau Hansen 


Virtual Infrastructure for Scientific Analysis (VISA) Workshop

We invite you to view our workshop – hosted jointly by ExPaNDS and PaNOSC! VISA provides remote data analysis services giving access to experimental data, analysis software, compute infrastructure and expert-user support (IT and Scientific). The online half-day Workshop is open to all external...

Keywords: VISA, expands, PaNOSC, remote data analysis services

Resource type: slides

Virtual Infrastructure for Scientific Analysis (VISA) Workshop https://pan-training.eu/materials/virtual-infrastructure-for-scientific-analysis-visa-workshop We invite you to view our workshop – hosted jointly by ExPaNDS and PaNOSC! VISA provides remote data analysis services giving access to experimental data, analysis software, compute infrastructure and expert-user support (IT and Scientific). The online half-day Workshop is open to all external stakeholders (especially scientists and IT staff but not limited to). We aim to demonstrate to beamline scientists the possibilities offered by VISA for data analysis and gathering IT staff around the table to discuss about the development and further deployment of the platform. VISA has been developed at a critical time (during the COVID-19 crisis) to answer data analysis needs and allowing remote instrument control. It provides simplified access for scientists to data analysis tools, offers remote support to users from experts and allows them to remotely control their experiments with the assistance of on-site instrument scientists. VISA allows a user to use the Remote Desktop as if they were sitting in front a data treatment workstation at the host institute and embeds JupyterLab directly accessible from the same platform interface. Initially developed at the Institut Laue-Langevin (ILL) in Grenoble, France and further deployed in the context of PaNOSC, VISA enables the PaN research infrastructures (PaN RIs) to work together on further scientific collaborations to solve 21st century challenges. The first session of the workshop showcases the implementation of VISA at the PaN RIs through ExPaNDS and PaNOSC partner’ demos and illustrate its use for scientific users and others. The second session in the afternoon focuses on presentations on VISA development and deployment (on OpenStack and other infrastructures), paving the way for a roundtable discussion on the future of VISA: sustainability and future collaborations. Video recording (direct link to vimeo) will be available soon. VISA, expands, PaNOSC, remote data analysis services
Machine learning in electronic-structure theory

Machine Learning approaches are increasingly being used across many fields of science, with electronic structure theory being no exception. They offer novel approaches to age-old problems. In recent years they have been used in construction of trial wavefunctions, in computing Hamiltonians, and...

Keywords: machine learning, electronic-structure theory

Resource type: video

Machine learning in electronic-structure theory https://pan-training.eu/materials/machine-learning-in-electronic-structure-theory Machine Learning approaches are increasingly being used across many fields of science, with electronic structure theory being no exception. They offer novel approaches to age-old problems. In recent years they have been used in construction of trial wavefunctions, in computing Hamiltonians, and in direct calculation of properties and forces. These methods are highly versatile and computationally efficient, yet many questions regarding their interpretability and ability to extrapolate information remains unanswered. How are they being used in electronic structure theory today, and how do they fit into the bigger picture? Would electronic structure theory have looked anything different if it was conceived in the age of machine learning? This seminar seeks to answer these kinds of questions, and was originally given as a trial lecture at the Hylleraas Centre of Quantum Molecular Sciences at the University of Oslo. machine learning, electronic-structure theory