Search Results for author:
Found 7 papers, 2 papers with code
A Machine-Learning-Ready Dataset Prepared from the Solar and Heliospheric Observatory Mission
We present a Python tool to generate a standard dataset from solar images that allows for user-defined selection criteria and a range of pre-processing steps.
Toward a Next Generation Particle Precipitation Model: Mesoscale Prediction Through Machine Learning (a Case Study and Framework for Progress)
We advance the modeling capability of electron particle precipitation from the magnetosphere to the ionosphere through a new database and use of machine learning (ML) tools to gain utility from those data.
Machine Learning in Heliophysics and Space Weather Forecasting: A White Paper of Findings and Recommendations
The authors of this white paper met on 16-17 January 2020 at the New Jersey Institute of Technology, Newark, NJ, for a 2-day workshop that brought together a group of heliophysicists, data providers, expert modelers, and computer/data scientists.
Dynamic Time Lag Regression: Predicting What & When
DTLR differs from mainstream regression and from sequence-to-sequence learning in two respects: firstly, no ground truth (e. g., pairs of associated sub-sequences) is available; secondly, the cause signal contains much information irrelevant to the effect signal (the solar magnetic field governs the solar wind propagation in the heliosphere, of which the Earth's magnetosphere is but a minuscule region).
Estimation of Accurate and Calibrated Uncertainties in Deterministic models
We show that for doing so, one has to compromise between the accuracy and the reliability (calibration) of such a probabilistic model.
A gray-box model for a probabilistic estimate of regional ground magnetic perturbations: Enhancing the NOAA operational Geospace model with machine learning
The model follows a 'gray-box' approach by combining the output of a physics-based model with machine learning.
Accuracy-Reliability Cost Function for Empirical Variance Estimation
In this paper we focus on the problem of assigning uncertainties to single-point predictions.
