HelioWebL. F. Guedes dos Santos
flowchart
A[L. F. Guedes dos Santos]
AC["Associated Concepts (25)"]
AW["Authored Works (13)"]
CA["Linked Co-Authors (23)"]
CI["Linked Collaborating Institutions (28)"]
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AW== author ==>CA
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click AW "#authored-works"
click CA "#co-authors"
click CI "#collaborating-institutions"
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- ORCiD
- https://orcid.org/0000-0001-5190-442X
- OpenAlex ID
- https://openalex.org/A5032171872 (API record)
Associated Concepts [?]
- Physics
- Quantum mechanics
- Computer science
- Magnetic field
- Astronomy
- Solar wind
- Plasma
- Nuclear physics
- Engineering
- Mathematics
- Geology
- Geography
- Coronal mass ejection
- Interplanetary spaceflight
- Materials science
- Artificial intelligence
- Organic chemistry
- Chemistry
- Astrophysics
- Meteorology
- Metallurgy
- Flux (metallurgy)
- Structural engineering
- Heliosphere
- Algorithm
Authored Works
sorted by decreasing year, and then by display-name
- The Unusual Widespread Solar Energetic Particle Event on 2013 August 19: Solar Origin and Particle Longitudinal Distribution
- Understanding the Internal Magnetic Field Structure of Interplanetary Coronal Mass Ejections Observed by Helios 1, Helios 2, and MESSENGER
- Self-supervised Deep Learning for Reducing Data Transmission Needs in Multi-Wavelength Space Instruments: a case study based on the Solar Dynamics Observatory.
- Identification of Flux Rope Orientation using Neural Networks
- Evidence of a complex structure within the 2013 August 19 CME. ICME radial and longitudinal evolution in the heliosphere.
- Direct First PSP Observation Of The Interaction Of Two Successive Interplanetary Coronal Mass Ejections On November 2020
- Unraveling the Internal Magnetic Field Structure of the Earth-directed Interplanetary Coronal Mass Ejections During 1995 - 2015
- Identifying Flux Ropes signatures using Deep Learning
- Auto-calibration and reconstruction of SDO's Atmospheric Imaging Assembly channels with Deep Learning
- Reconstruction of the near-Earth interplanetary coronal mass ejections during 1995-2015: Catalog of geometrical and physical properties
- How can machine learning help us to analyze CME's data?
- Analysis of Interplanetary Coronal Mass Ejections (ICMEs) Observed by Wind, DSCOVR and STEREO
- ForeCAT: Using CME Deflections to Constrain their Mass and the Drag
Linked Co-Authors
- Atılım Güneş Baydin
- Ayris Narock
- D. Lario
- Daniel B. Seaton
- David R. Williams
- Erika Palmerio
- Fernando Carcaboso
- I. Zouganelis
- J. C. Kasper
- J. Rodrı́guez-Pacheco
- L. K. Jian
- M. L. Mays
- Mateja Dumbović
- Meng Jin
- Miho Janvier
- N. Nitta
- Nathalia Alzate
- Phillip Hess
- S. D. Bale
- Suchismita Bose
- T. W. Narock
- Tatiana Niembro
- Teresa Nieves‐Chinchilla
Linked Collaborating Institutions
- ADNET Systems Inc., Greenbelt, Maryland
- Boston University, Massachusetts
- Catholic University, Washington DC
- European Science and Astronomy Center
- George Mason University, Virginia
- Goucher College, Maryland
- Institut d'Astrophysique Spatiale
- Johns Hopkins University, Applied Physics Laboratory
- Katholieke University of Leuven, Belgium
- Lockheed Martin, Palo Alto
- NASA Goddard Space Flight Center, Maryland
- National Autonomous University of Mexico
- SETI Institute, California
- Smithsonian Astrophysical Observatory
- Southwest Research Institute, Boulder
- University of Alcala, Spain
- University of Brasilia, Brazil
- University of California, Berkeley
- University of Malaga, Spain
- University of Maryland, Baltimore County
- University of Michigan
- University of Oslo, Institute of Theoretical Astrophysics
- University of Oslo, Norway
- University of Oxford, Department of Engineering
- University of Oxford, UK
- University of Sao Paulo, Brazil
- University of Turku, Finland
- University of Zagreb, Croatia
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