Convolutional Autoencoder

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1 paper in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

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PISCES: Physics-Informed Solar-wind Convolutional autoEncoder for Space-weather Anomaly Detection and Early Warning
Sep 23, 2026Kevin Lee, Alison J. MarchInterpretable Anomaly Detection Convolutional Autoencoder
NASA Jet Propulsion Laboratory, La Cañada Flintridge, CA, USA · Department of Mechanical and Aerospace Engineering, UCLA, Los Angeles, CA, USA · College of Engineering & Applied Science, University of Colorado, Boulder, CO, USA
Physics-informed distribution of relaxation times estimation and latent-space condition monitoring of solid oxide fuel and electrolysis cells from electrochemical impedance spectroscopy
Aug 13, 2026Žan Gorenc, Žiga Gradišar, Felix Mütter +2Convolutional Autoencoder Physics-Informed ML
Jožef Stefan International Postgraduate School, Jamova cesta 39, Ljubljana, 1000, Slovenia · Institute of Thermal Engineering, Graz University of Technology, Inffeldgasse 25/B, Graz, 8010, Austria · Jožef Stefan Institute, Jamova cesta 39, Ljubljana, 1000, Slovenia +1
The balance between compactness and forecast accuracy of data-driven latent-space reduced-order models in controlled wake flows
Jul 27, 2026Alberto Solera-Rico, Patricia García-Caspueñas, Carlos Sanmiguel Vila +1Variational Autoencoders Latent Dynamics Modeling
Sub-directorate general of aeronautical systems, Spanish National Institute for Aerospace Technology (INTA), ctra. M-301, km. 10.5, 28330 San Mart´ın de la Vega, Madrid, Spain. · Departamento de Ingenier´ıa Aeroespacial, Universidad Carlos III de Madrid, Avenida de la Universidad 30, 28911 Legan´es, Madrid, Spain. · Department of Mechanical Engineering, University of Washington, 1410 NE Campus Parkway, Seattle, WA 98195, USA.
A Multi-Fidelity Convolutional Autoencoder-Transfer Learning Framework for Guided-Wave-Based Damage Diagnosis Using Large Simulated and Limited Experimental Datasets
Jun 25, 2026Santosh Kapuria, AbhishekConvolutional Autoencoder Transfer Learning
Department of Applied Mechanics, Indian Institute of Technology Delhi, New Delhi, 110016, Delhi, India
A self-supervised learning approach to deep filter banks for texture recognition
May 27, 2026Joao B. Florindo, Lucas O. Lyra, Antonio E. FabrisAutoencoders Convolutional Autoencoder
Institute of Mathematics, Statistics and Scientific Computing of the University of Campinas, Rua Sergio Buarque de Holanda, 651, Campinas, 13083-859, Sao Paulo, Brazil · Institute of Mathematics and Statistics of the University of Sao Paulo, Rua do Matao, 1010, Sao Paulo, 05508-090, Sao Paulo, Brazil
A Practical Noise2Noise Denoising Pipeline for High-Throughput Raman Spectroscopy
May 18, 2026David Martin-Calle, Cesar Alvarez Llamas, Vincent Motto- Ros +3Denoising Autoencoders Convolutional Autoencoder
ILM,UCBL,CNRS · Universit´e Claude Bernard Lyon 1, CNRS, Institut Lumi`ere Mati`ere, F-69622 Villeurbanne, France · ILM,UCBL,CNRS,IUF +1
Safe Aerial 3D Path Planning for Autonomous UAVs using Magnetic Potential Fields
May 11, 2026Haechan Mark Bong, Giovanni BeltrameUAV Navigation Convolutional Autoencoder
Department of Computer Engineering and Software Engineering, Polytechnique Montréal, QC., Canada. · MILA, QC., Canada.
Focus on What Matters: Two-Stage ROI-Aware Refinement for Anatomy-Preserving Fetal Ultrasound Reconstruction
Apr 26, 2026Ines Abbes, Mahmood Alzubaidi, Mowafa Househ +3Autoencoders Image Reconstruction
College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar · Sidra Medicine, Doha, Qatar
PAWN: Piece Value Analysis with Neural Networks
Apr 16, 2026Ethan Tang, Hasan Davulcu, Jia Zou +1Convolutional Autoencoder Chess
Arizona State University, Tempe AZ 85281, USA
A convolutional autoencoder and neural ODE surrogate modeling framework applied to transient counterflow flames
Mar 16, 2026Mert Yakup Baykan, Weitao Liu, Mohammad Rafi Malik +4Autoencoders PDE Surrogate Modeling
Department of Aerospace Engineering, KAIST, Daejeon, Republic of Korea · Clean Energy Research Platform (CERP), KAUST, Thuwal, Saudi Arabia · Institute for Reactive Flows (IRST), University of Stuttgart, Pfaffenwaldring 31, 70569 Stuttgart, Germany
Surrogate Modeling of 3D Rayleigh-Benard Convection with Equivariant Autoencoders
Date pendingFynn Fromme, Hans Harder, Christine Allen-Blanchette +1Autoencoders PDE Surrogate Modeling
Lamarr Institute for Machine Learning and Artificial Intelligence and Karlsruhe Institute of Technology · Lamarr Institute for Machine Learning and Artificial Intelligence and Department of Computer Science at Paderborn University · Department of Mechanical and Aerospace Engineering Center for Statistics and Machine Learning Princeton University +1

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PISCES: Physics-Informed Solar-wind Convolutional autoEncoder for Space-weather Anomaly Detection and Early Warning

Kevin Lee, Alison J. March

NASA Jet Propulsion Laboratory, La Cañada Flintridge, CA, USA · Department of Mechanical and Aerospace Engineering, UCLA, Los Angeles, CA, USA · College of Engineering & Applied Science, University of Colorado, Boulder, CO, USA

Interpretable Anomaly Detection Convolutional Autoencoder Unsupervised Anomaly Detection Physics-Informed ML Anomaly Detection

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Space weather early warning depends on detecting solar wind transients in in-situ measurements at the first Sun-Earth Lagrange point (L1), before they reach Earth. Fixed thresholds can miss combined magnetic and plasma structure, and many learning methods provide a single anomaly score. We present the Physics-Informed Solar-wind Convolutional autoEncoder for Space-weather (PISCES), a convolutional autoencoder trained without catalog labels on OMNI solar wind measurements under physics constraints. Its loss includes magnetic field consistency, an empirical relation between temperature and velocity, the Parker spiral angle, and penalties on changes between consecutive one-minute samples in derived quantities calculated from the reconstruction. At inference, PISCES separates the anomaly score into magnetic and plasma reconstruction errors, physics relations, and residual corrections, and reports the magnitude of each contribution. Attenuation of the skip connections, selected on validation data, improves average precision for the trained models, while the untrained scores remain nearly the same. The trained models also give a more consistent ordering of these physical contributions. After smoothing with a trailing median, the alarms can precede independently observed sudden commencements, including positive sudden impulses.

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