#deep learning
82 papers · 1 filter
Reliability-calibrated deep residual full-waveform inversion using geometry-invariant physics encoding: synthetic validation and zero-shot Marmousi-2 testing
Deepak Kumar, Jayant Nath Tripathi, Laxmidhar Behera
The paper presents a deep learning pipeline that uses physics‑derived inputs to correct full‑waveform inversion results and provides calibrated, pixel‑wise uncertainty estimates th…
What to Remove, What to Preserve: Dual-Ambiguity Rectification for All-in-One Image Restoration
Cencen Liu, Wen Yin, Dongyang Zhang +6
The paper introduces DAR-Net, a deep network that tackles the dual ambiguity problem in all‑in‑one image restoration by modeling degradation states with a simplex‑constrained arche…
Large scale cross-regional remote sensing flood monitoring framework for operative mapping and impact analysis
Ilya Novikov, Svetlana Illarionova, Ruslan Dzharkinov +6
The paper proposes an end‑to‑end multimodal deep‑learning framework that combines SAR, multispectral and elevation data to detect flood water surfaces and assess damage across larg…
Same Branches, Different Trees: A Bifurcation Connectedness Metric for Coronary Artery Segmentation and FFR-CT Decision Agreement
Maame Owusu-Ansah, Kelvin Lee, Dr Vinod Venugopal +3
The paper introduces a Bifurcation Connectedness Score (BCS) to evaluate how well coronary artery segmentations preserve the connectivity of vessel bifurcations, showing that highe…
Uncertainty quantification for trustworthy deep learning: Methods and measures
H. Martin Gillis, Thomas Trappenberg
The paper surveys methods for quantifying uncertainty in deep neural networks, focusing on ensemble-based and approximate Bayesian approaches and how their outputs are measured.
Weather Emulators at the Frontier of Heat Extremes Predictability
Cas Decancq, Thomas Mortier, Jessica Keune +1
The paper compares six modern deep‑learning weather emulators with traditional dynamical and statistical models for forecasting global near‑surface temperature and extreme heat at…