On-device research index

arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

168,742 papers · 148 categories

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8 results for “Ionosphere”

Real-time Ionospheric Imaging of S4 Scintillation from Limited Data with Parallel Kalman Filters and Smoothnessphysics.ao-ph

Real-time ionospheric images created from limited data using parallel Kalman filters.

problem Monitoring ionospheric irregularities using limited spatio-temporal observations.

method Bayesian framework with parallel Kalman filters and connectivity information.

result Real-time ionospheric images with high spatio-temporal resolution can be produced.

ionospheric imaging S4 scintillation Bayesian filtering parallel Kalman filters connectivity information

2021-05-11abs ↗pdf ↗

Ionospheric Scintillation Forecasting Using Machine Learningeess.SP

Study uses ML to forecast ionospheric scintillation severity.

problem Predicting amplitude scintillation severity using real-time data.

method Developed and tested six ML models, XGBoost being the most effective.

result XGBoost model achieved 77% prediction accuracy for scintillation severity.

ionospheric scintillation machine learning GNSS amplitude fluctuations S4 index

2024-08-28abs ↗pdf ↗

Prediction of GNSS Phase Scintillations: A Machine Learning Approachcs.LG

Paper predicts GNSS phase scintillations with machine learning.

problem Predicting phase scintillations due to ionosphere disturbances.

method Proposes a novel machine learning architecture and loss function.

result Achieves state-of-the-art prediction of phase scintillations 1 hour in advance.

GNSS Phase Scintillations Machine Learning Ionosphere Space Weather

2019-10-03abs ↗pdf ↗

Toward a Next Generation Particle Precipitation Model: Mesoscale Prediction Through Machine Learning (a Case Study and Framework for Progress)physics.space-ph

New model predicts particle precipitation from magnetosphere to ionosphere.

problem Improving prediction of electron particle precipitation from magnetosphere to ionosphere.

method Compilation of new database, use of machine learning (ML) tools, neural network (PrecipNet).

result PrecipNet achieves >50% reduction in errors and better captures dynamic changes.

particle precipitation neural network machine learning space weather ionosphere

2020-11-19abs ↗pdf ↗

Reliable Prediction Intervals with Regression Neural Networkscs.LG

The paper improves NN predictions with reliable confidence intervals.

problem Creating accurate prediction intervals for neural network outputs.

method Introduces Conformal Prediction (CP) for NNs, ensuring reliable confidence measures.

result The proposed method produces well-calibrated and tight prediction intervals.

prediction intervals neural networks conformal prediction machine learning confidence measures

2023-12-15abs ↗pdf ↗

Performance Evaluation of t-SNE and MDS Dimensionality Reduction Techniques with KNN, ENN and SVM Classifierscs.LG

This paper evaluates t-SNE and MDS for reducing dimensions in datasets and classifying them with KNN, ENN, and SVM.

problem Reducing dimensions in datasets for better classification performance.

method t-SNE and MDS applied to nine datasets, followed by KNN, ENN, and SVM classification.

result Performance comparison of t-SNE and MDS with KNN, ENN, and SVM.

dimensionality reduction t-SNE MDS KNN ENN

2020-06-20abs ↗pdf ↗

Learning Context-conditioned Gaussian Overbounds for Convolution-Based Uncertainty Propagationcs.LG

Unified neural network framework for context-aware Gaussian overbounds in uncertainty propagation.

problem Uncertainty quantification in safety-critical settings requires conservative bounds, but existing methods often fail to compose and are overly conservative.

method Proposes a learning framework that trains neural networks to produce context-aware Gaussian overbounds with provable conservatism.

result The method yields tighter bounds while maintaining conservatism on the enforced grid and in experiments.

uncertainty quantification Gaussian overbounds convolution conservatism neural networks

2026-05-15abs ↗pdf ↗

Robust Gaussian Process Regression for Real-Time High Precision GPS Signal Enhancementstat.ML

Satellite-based positioning system such as GPS often suffers from large amount of noise that degrades the positioning accuracy dramatically especially in real-time applications. In this work, we consider a data-mining approach to enhance the GPS signal. We build a large-scale high precision GPS receiver grid system to …

2019-06-03abs ↗pdf ↗