Physics-Informed Neural Networks and Data-Driven Models for GRB X-ray Light-Curve Gap Reconstruction

arXiv:2609.15261 · astro-ph.HE, astro-ph.CO · Submitted 2026-09-14 · Read on arXiv

astro-ph.HE, astro-ph.CO

Submitted: 2026-09-14

Updated: 2026-09-14

Comments: 36 pages, 6 figures, 7 tables, journal paper

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Benchmark — 8× in this paper · explained in 2 episodes
• Epistemic Uncertainty — 8× in this paper · explained in 10 episodes
• Aleatoric Uncertainty — 6× in this paper · explained in 5 episodes
• Physics-Informed Neural Network — 6× in this paper
• Robustness — 6× in this paper · explained in 5 episodes
• Activation Function — 5× in this paper · explained in 1 episode
• Uncertainty Quantification — 4× in this paper · explained in 8 episodes
• Consistency — 3× in this paper · explained in 3 episodes
• Gradient Flow — 3× in this paper · explained in 2 episodes
• Accuracy — 2× in this paper · explained in 1 episode
• Adam — 2× in this paper · explained in 2 episodes
• Calibration — 2× in this paper · explained in 7 episodes
• Core — 2× in this paper · explained in 2 episodes
• Cubic Smoothing Spline — 2× in this paper
• Degeneracy — 2× in this paper · explained in 3 episodes
• Embeddings — 2× in this paper · explained in 2 episodes
• Feature Engineering — 2× in this paper · explained in 3 episodes
• Fidelity — 2× in this paper · explained in 2 episodes
• Latent Space — 2× in this paper · explained in 7 episodes
• Loss Function — 2× in this paper · explained in 2 episodes
• Monte Carlo — 2× in this paper
• Physics-Informed Neural Network (PINN) — 2× in this paper · explained in 3 episodes
• Quartic Smoothing Spline — 2× in this paper
• Representation Learning — 2× in this paper · explained in 2 episodes
• Symbolic Regression — 2× in this paper · explained in 5 episodes
• Bayesian Neural Network — 1× in this paper
• Conformal Prediction — 1× in this paper · explained in 5 episodes
• Council of Scientific and Industrial Research — 1× in this paper
• Data Augmentation — 1× in this paper · explained in 7 episodes
• Deep Gaussian Process — 1× in this paper
• Fisher Information — 1× in this paper · explained in 3 episodes
• Gaussian Processes (GPs) — 1× in this paper · explained in 2 episodes
• Latent Representation — 1× in this paper · explained in 2 episodes
• Latent Representations — 1× in this paper · explained in 3 episodes
• Monte Carlo dropout — 1× in this paper · explained in 2 episodes
• Objective Function — 1× in this paper · explained in 2 episodes
• Over-smoothing — 1× in this paper · explained in 4 episodes
• Overview of Uncertainty Quantification — 1× in this paper
• Physical Constraints — 1× in this paper · explained in 7 episodes
• Physics-Informed Neural Networks (PINNs) — 1× in this paper · explained in 6 episodes
• Swift X-ray Telescope — 1× in this paper
• Systematic Bias — 1× in this paper · explained in 3 episodes
• Temporal Convolutional Network — 1× in this paper

Sources

• Optuna: A Next-generation Hyperparameter Optimization Framework
• VLT observations of GRB 990510 and its environment
• Scientific Machine Learning through Physics-Informed Neural Networks: Where we are and What's next
• Gamma-ray burst afterglows and evolution of postburst fireballs with energy injection from strongly magnetic millisecond pulsars
• Gamma-Ray Bursts as an Independent High-Redshift Probe of Dark Energy
• The two-dimensional and three-dimensional relations in the plateau emission in multi-wavelengths
• Concrete Dropout
• Gaussian Error Linear Units (GELUs)
• What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?
• Adam: A Method for Stochastic Optimization
• Self-Normalizing Neural Networks
• Decoupled Weight Decay Regularization
• On the difficulty of training Recurrent Neural Networks
• The Deep and Transient Universe in the SVOM Era: New Challenges and Opportunities - Scientific prospects of the SVOM mission

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