Description
Leader of the group Multiscale Dynamics: Ute Ebert.
Nature and technology are full of dynamics, often involving multiple scales in length, time and energy. To model these processes, we combine scientific computing with model reduction and machine learning, with particular focus on plasma dynamics in lightning and space weather, and in high voltage and plasma technology.
Our research addresses questions in nature such as start and propagation of lightning strokes, terrestrial gamma-ray flashes and space weather, and closely related technological problems such as switch gear for long-distance electricity nets, air purification and disinfection with corona reactors, and protection of satellites and electricity nets from space weather.
Within national and European projects, we collaborate with colleagues in applied plasma physics, electrical and mechanical engineering, atmospheric electricity, and cosmic particle and space science, and with non-academic partners such as ABB, DNV-GL, ESA-ESTEC and NASA.
Find more about our work (including publications) on the personal homepages of the staff scientists:
- Ute Ebert (group leader)
- Enrico Camporeale
- Jannis Teunissen
and on the page with our numerical codes for Multiscale Plasma Dynamics.
View a photo of the Multiscale Dynamics group.
Vacancies
Postdoc on the subject of Machine Learning in Space Physics
Centrum Wiskunde & Informatica (CWI) has a vacancy in the Multiscale Dynamics research group for a talented Postdoc, on the subject of Machine Learning in Space Physics.
News
Plasma for Plants technology project granted
Recently researchers from CWI and TU/e were awarded an OTP technology grant to understand the effect of plasma on plants.
CWI joins ESCAPE project to build European Open Science Cloud
CWI will participate in the ESCAPE project, which is part of the effort to build a European Open Science Cloud (EOSC), which will allow universal access to scientific data through a single online platform.
CWI group leader Ute Ebert on Focus TV
Ute Ebert, group leader of CWI’s Multiscale Dynamics group, appeared on national TV in the NTR science television programme Focus, where she explained which physical phenomena cause the beginning of lightning.
Machine Learning Techniques for Space Weather book published
Recently, the book 'Machine Learning Techniques for Space Weather', edited by CWI researcher Enrico Camporeale and others, was published by Elsevier. The book provides “a thorough and accessible presentation of machine learning techniques that can be employed by space weather professionals”.
Members
Associated Members
Publications
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Camporeale, E, Chu, X, Agapitov, O.V, & Bortnik, J. (2019). On the generation of probabilistic forecasts from deterministic models. Space Weather. doi:10.1029/2018SW002026
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Diniz, G, Rutjes, C, Ebert, U, & Ferreira, I.S. (2019). Cold electron runaway below the friction curve. Journal of Geophysical Research - Atmospheres, 124(1), 189–198. doi:10.1029/2018JD029178
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Sarria, D, Rutjes, C, Diniz, G, Luque, A, Ihaddadene, K.M.A, Dwyer, J.R, … Ebert, U. (2018). Evaluation of Monte Carlo tools for high-energy atmospheric physics II: Relativistic runaway electron avalanches. Geoscientific Model Development, 11(11), 4515–4535. doi:10.5194/gmd-11-4515-2018
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Johnson, J.R, Wing, S.P, & Camporeale, E. (2018). Transfer entropy and cumulant-based cost as measures of nonlinear causal relationships in space plasmas: Applications to Dst. Annales Geophysicae, 36(4), 945–952. doi:10.5194/angeo-36-945-2018
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Gruet, M.A, Chandorkar, M.H, Sicard, A, & Camporeale, E. (2018). Multiple-hour-ahead forecast of the Dst index using a combination of Long Short-Term Memory neural network and Gaussian process. Space Weather. doi:10.1029/2018SW001898
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Agnihotri, A. (2018, June 25). Modeling heat dominated electrical breakdown.
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Elkholy, A, Nijdam, S, van Veldhuizen, E.M, Dam, N, van Oijen, J, Ebert, U, & de Goey, L.P.H. (2018). Characteristics of a novel nanosecond DBD microplasma reactor for flow applications. Plasma Sources Science and Technology, 27. doi:10.1088/1361-6595/aabf49
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Rutjes, C. (2018, March 15). Modeling high energy atmospheric physics and lightning inception.
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Kwon, D.-H, Lee, W, Preval, S, Ballance, C.P, Behar, E, Colgan, J, … Braams, B.J. (2018). Iso-nuclear tungsten dielectronic recombination rates for use in magnetically-confined fusion plasmas. Atomic Data and Nuclear Data Tables, 119(January 2018), 250–262.
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Camporeale, E, Carè, A, & Borovsky, J.E. (2017). Classification of solar wind with machine learning. Journal of Geophysical Research: Space Physics, 122(11), 10910–10920. doi:10.1002/2017JA024383
Software
Afivo: A framework for adaptive mesh refinement with geometric multigrid methods
Afivo, short for Adaptive Finite Volume Octree, is a framework for adaptive mesh refinement on quadtree (2D) and octree (3D) meshes with shared memory parallelization (OpenMP) and geometric multigrid methods.
PumpKin: Analyzing complex chemistry models
PumpKin, short for Pathway Reduction Method for Plasma Kinetic Models, is a tool for post-processing results from zero-dimensional plasma kinetics solvers.
Current projects with external funding
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Making plasma-assisted combustion efficient ()
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Real-time forecasting of killer electrons on satellite orbits ()
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Artificial Intelligence Data Analysis (AIDA)
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European Science Cluster of Astronomy & Particle physics ESFRI research infrastructures (ESCAPE)
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Let CO2 spark! Understand breakdown dynamics for high voltage technology and lightning Abstract Sparks, (Let CO2 spark!)
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Plasma for Plants: Towards controlled and efficient plasma-activated water generation for a cleaner environment (Plasma for Plants)
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Science and Innovation with Thunderstorms (SAINT)
Related partners
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CNRS
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CSIC
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IT-DTU Lyngby
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Universita di Pisa
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Universitetet i Bergen
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University of Bath
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Bristol Industrial and Research Associates Limited
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DENA DESARROLLOS SL
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Katholieke Universiteit Leuven
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Technische Universiteit Eindhoven
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Universidad Politecnica Catalunya
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Université Paul Sabatier (Toulouse III)