1 citations · 2 across the 8 of their papers we have counts for
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Hyperbolic Time Series Forecasting with Learnable Decomposition and Manifold Dynamics
Sarang Rajendra Patil, Vishal Fenn, Mengjia Xu
Multiscale structure is central to time series forecasting: temporal signals contain slowly varying trends, intermediate seasonal patterns, and high-frequency residuals. Traditiona…
Multilevel Graph Wavelet Compressed Sensing with Scale-Aware Neural Recovery
Amirhossein Nouranizadeh, Sarang Rajendra Patil, Alan John Varghese +3
Scientific machine learning methods such as neural operators and physics-informed neural networks have advanced engineering applications and inverse problems, but their training ty…
Prediction of Magnetic Flux Evolution During Solar Active Region Emergence using Long Short-Term Memory Networks
Eren Dogan, Spiridon Kasapis, Sarang Patil +5
Solar active regions (ARs) are the primary drivers of space weather events, making their early prediction crucial for operational forecasting systems. We develop machine learning m…
SolARED: Solar Active Region Emergence Dataset for Machine Learning Aided Predictions
Spiridon Kasapis, Eren Dogan, Irina N. Kitiashvili +6
The development of accurate forecasts of solar eruptive activity has become increasingly important for preventing potential impacts on space technologies and exploration. Therefore…
Forecasting Continuum Intensity for Solar Active Region Emergence Prediction using Transformers
Jonas Tirona, Sarang Patil, Spiridon Kasapis +5
Early and accurate prediction of solar active region (AR) emergence is crucial for space weather forecasting. Building on established Long Short-Term Memory (LSTM) based approaches…