arXiv AI
Jul 7
Hierarchical Multi-to-Single-Modal Knowledge Distillation for Disruption Prediction in EAST
arXiv:2607. 04241v1 Announce Type: cross Abstract: Plasma disruption is a critical threat to tokamak safety.
Visible-Light Imaging Diagnosis of Neutral Particle Emission Tomography in the Tokamak Divertor: An Efficient Transformer-based Surrogate Model
arXiv:2607. 22704v1 Announce Type: cross Abstract: Nuclear fusion has made significant progress in recent years and is expected to become one of the most important pathways to addressing global energy challenges.
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arXiv Machine Learning
Jul 23
A Deep Learning Framework for Predicting Solar EUV Irradiance During Significant Flares
arXiv:2607. 19597v1 Announce Type: cross Abstract: We present FlareEUV, a multimodal deep learning framework for predicting daily extreme ultraviolet (EUV) irradiance at 6.
arXiv Machine Learning
1d ago
Data-Driven Reconstruction of Spatially Resolved Electron and Ion Energy Distributions from Macroscopic Plasma Quantities with Deep Neural Networks
arXiv:2608. 16519v1 Announce Type: cross Abstract: Spatially resolved EEDFs/IEDFs provide essential kinetic information about low-temperature plasmas (LTPs) and play a central role in determining transport, chemical reaction rates, and plasma surface interactions.
arXiv AI
Jun 8
TokaMind: A Multi-Modal Transformer Foundation Model for Tokamak Plasma Dynamics
arXiv:2602. 15084v2 Announce Type: replace-cross Abstract: We present TokaMind, to our knowledge the first open-source foundation model for tokamak plasma dynamics, based on a Multi-Modal Transformer (MMT) and pretrained on heterogeneous diagnostics from the publicly available MAST dataset.
Hugging Face Trending Papers
2d ago
Data-Driven Reconstruction of Spatially Resolved Electron and Ion Energy Distributions from Macroscopic Plasma Quantities with Deep Neural Networks
Spatially resolved EEDFs/IEDFs provide essential kinetic information about low-temperature plasmas (LTPs) and play a central role in determining transport, chemical reaction rates, and plasma surface interactions. While kinetic simulations directly resolve these distributions, experimental measurements remain challenging and are often invasive, spatially limited, or require assumptions regarding the distribution shape such as a Maxwellian.
arXiv AI
Aug 7
SafeDivertor: Faithful Divertor Heat Flux Reconstruction from Macroscopic Plasma State Signals via Time-Frequency Prior Exploitation
arXiv:2608. 05669v1 Announce Type: cross Abstract: Divertor heat-flux analysis is essential for understanding plasma-wall interactions and protecting plasma-facing components in magnetic-confinement fusion devices, while conventional infrared-based inversion is usually performed after discharge and requires heat-conduction modeling with device-specific material properties, divertor geometry, and boundary conditions.
arXiv Machine Learning
Jul 7
SAF3R: Dynamic Sparse Attention for Feed-Forward 3D Reconstruction Transformers
arXiv:2607. 03612v1 Announce Type: cross Abstract: Feed-forward 3D reconstruction (F3R) transformers have recently achieved remarkable success.
arXiv AI
Jul 7
MambaLIE: Scene Light Intensity-Boosted Low-Light Image Enhancement with State Space Model
arXiv:2607. 03013v1 Announce Type: cross Abstract: Images captured by consumer electronic devices, such as mobile phones and digital cameras, often suffer from low-light degradation due to sensor limitations and imaging pipelines, which degrades visual quality and affects downstream vision tasks.
arXiv AI
Jun 16
JetParticle-JEPA: An Efficient Self-Supervised Representation Learning method for Jet Tagging in High-Energy Physics
arXiv:2606. 14813v1 Announce Type: cross Abstract: Jet tagging at the Large Hadron Collider increasingly relies on deep learning models trained on massive simulated datasets, leading to high computational costs and limited robustness to detector mismodeling.
Hugging Face Trending Papers
Jun 22
Ultra-Peripheral Collisions as a Nuclear-Structure Interferometer with Interpretable Multitask Deep Learning
Precise knowledge of nuclear structure is essential across fundamental physics, yet probing these structures is notoriously difficult. To address this challenge, ultra-peripheral collisions (UPCs) provide a femtoscopic tomography for imaging the atomic nucleus.
arXiv Machine Learning
Jun 2
Probabilistic Data-Driven Modelling of Astrophysical Transients: The Neural Process Family for Ultrafast and Class-Agnostic Light Curve Reconstruction with NightLANP
arXiv:2605. 27527v2 Announce Type: replace-cross Abstract: Astrophysical observations from Earth are subject to weather, environmental, and scientific constraints that lead to sparse, irregular light curves.
arXiv AI
Jun 17
Spatio-Temporal Fusion Model for Standard View Classification of Echocardiographic Videos
arXiv:2606. 17437v1 Announce Type: cross Abstract: Automated classification of standard echocardiographic views is crucial for efficient clinical workflow but faces three main challenges.