HelioWebV. A. Pinto
flowchart
A[V. A. Pinto]
AC["Associated Concepts (25)"]
AW["Authored Works (9)"]
CA["Linked Co-Authors (6)"]
CI["Linked Collaborating Institutions (6)"]
A== dcterms:relation ==>AC
AW== author ==>A
AW== author ==>CA
AW== affil ==>CI
click AC "#associated-concepts"
click AW "#authored-works"
click CA "#co-authors"
click CI "#collaborating-institutions"
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- ORCiD
- https://orcid.org/0000-0003-1210-167X
- OpenAlex ID
- https://openalex.org/A5027312777 (API record)
Associated Concepts [?]
- Physics
- Quantum mechanics
- Nuclear physics
- Magnetic field
- Geology
- Plasma
- Geography
- Meteorology
- Magnetosphere
- Geophysics
- Astronomy
- Solar wind
- Earth's magnetic field
- Geomagnetic storm
- Van Allen radiation belt
- Thermodynamics
- Mathematics
- Engineering
- Atmospheric sciences
- Electron
- Optics
- Mechanics
- Environmental science
- Computer science
- Aerospace engineering
Authored Works
sorted by decreasing year, and then by display-name
- Investigating the Influence of Inner Magnetosphere Data on a Regional Geomagnetically Induced Current Forecasting Model
- Forecasting of Extreme Ground Magnetic Field Fluctuations at Mid-Latitudes using Machine Learning
- Forecasting Ground Magnetic Perturbations at High and Mid-Latitudes Using Deep Learning and Near Real-Time Solar Wind Data
- Constructing a Machine Learned Global Geomagnetic Field Prediction Model from Magnetic Local Time Dependent Multi-Layer Perceptron Neural Networks
- Characterizing Geomagnetic Storm Data for Machine Learning Models of Geomagnetically Induced Currents
- Using Convolutional Neural Networks and Long-Short Term Machine Learning Models to Provide Insights into GIC Drivers and Risk of Occurrence.
- Implementing an Ensemble Model Approach for Geomagnetic Field Disturbance Predictions in Alaska utilizing Multivariate LSTM Neural Networks
- Forecasting Ground-Level Magnetic Perturbations Using a Spherical Elementary Current System
- Establishing a benchmark for ground magnetic field perturbations predictions using machine learning models
Linked Co-Authors
Linked Collaborating Institutions
- Atmospheric & Space Technology Research Associates (ASTRA)
- NASA Goddard Space Flight Center, Maryland
- University of Alaska, Fairbanks
- University of Alaska, Fairbanks, Geophysical Institute
- University of California, Los Angeles
- University of New Hampshire
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