Convolutional LSTM

LSTM: Long Short-Term Memory

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Sep 29, 2026physics.plasm-ph

A Pre-trained Variational Autoencoder for Gyrokinetic Plasma Turbulence Surrogate Modeling

Machine learning surrogate models offer a promising path toward accelerating plasma turbulence simulations. We present PreVAE-Turb, a surrogate modeling framework that leverages pre-trained variational autoencoders (VAEs) from the Stable Diffusion image generation model for efficient spatial compression of turbulence fields. The pre-trained VAE is fine-tuned on turbulence data using a physics-informed loss function that includes a spectral loss operating in Fourier space to enforce spectral accuracy across scales. The VAE is combined with convolutional long short-term memory (ConvLSTM) networks to learn temporal dynamics in latent space, with a manifold consistency error metric that monitors encode--decode consistency during autoregressive rollouts. We validate the framework on two-dimensional Hasegawa-Wakatani drift-wave turbulence and extend it to gyrokinetic turbulence from the GENE code, where a four-channel adaptation simultaneously predicts electrostatic potential, density, and parallel/perpendicular temperature fluctuations without requiring architecture redesign. Once trained, inference generates thousands of time steps in seconds on a single GPU, providing substantial computational acceleration compared to direct numerical simulation. The pre-trained approach offers a transferable methodology broadly applicable to various turbulence simulation codes.

Minglei Yang, Marshall Nicholson, Diego Del-Castillo-Negrete +2

Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA · Grand Valley State University, Allendale, MI, 49401, USA · University of Texas at Austin, Austin, TX, 78712, USA

Variational AutoencodersPhysics-Informed Generative ModelingSurrogate ModelingPDE Surrogate ModelingConvolutional LSTM
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Sep 28, 2026cs.CV

Temporal Modelling for Burn Scars on Sentinel-3

Rapid and accurate burn scar delineation from satellite imagery is essential for post-fire damage assessment. Sentinel-3 OLCI, with daily revisit and 21 spectral bands, suits rapid mapping, yet most pipelines treat acquisitions independently, leaving the pre/post-fire change signal unexploited. We present a dataset of 246 wildfire activations (2016-2025) from the Copernicus Emergency Management Service, with Sentinel-3 OLCI temporally paired acquisitions. We benchmark spatial and temporal (ConvLSTM-augmented) variants of three backbones (U-Net, SegFormer, ConvNeXt-UPerNet) under two input modes and spectral configurations. Temporal modeling improves segmentation only when pre-fire frames are included, and a 5-band subset matches the full 21-band OLCI configuration.

Luca Barco, Edoardo Arnaudo, Andrea Bragagnolo +2

Politecnico di Torino, Torino, Italy · Fondazione LINKS, Torino, Italy

Remote Sensing Image SegmentationDisaster Damage AssessmentConvolutional LSTMRemote SensingRemote Sensing Benchmarks
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Jul 29, 2026cs.LG

Benchmarking ConvLSTM for One-Day-Ahead IMDAA Rainfall-Field Prediction across Four Indian Cities

Convolutional long short-term memory networks (ConvLSTMs) are widely used for precipitation forecasting, but most evidence for their performance comes from dense, high-frequency radar sequences. This study tests whether convolutional recurrence improves one-day-ahead rainfall-field prediction on small daily reanalysis grids. Indian Monsoon Data Assimilation and Analysis (IMDAA) fields for June-September 1998-2020 were analysed for Bengaluru, Delhi, Kolkata and Mumbai. Ten naive, statistical, tree-based and neural approaches were compared using atmospheric-only and rainfall-history-plus-atmospheric inputs. Performance was assessed for complete fields, domain-mean rainfall, spatial anomalies and high-rainfall days. ConvLSTM did not consistently outperform simpler alternatives. FC-LSTM produced the numerically lowest domain-mean rainfall error in Bengaluru, Kolkata and Mumbai, whereas persistence performed best in Delhi. ConvLSTM produced the numerically lowest spatial-anomaly error only in Mumbai, where rainfall fields showed greater short-term spatial continuity and rainfall-history inputs improved all three neural architectures. The difference between ConvLSTM and FC-LSTM was nevertheless small. Neural models underestimated rainfall magnitude and predicted too few threshold exceedances on high-rainfall days, while persistence achieved the highest detection performance in every city. Post-hoc analyses showed that the selected models were most sensitive to the latest input day, with broader recent-lag sensitivity in Mumbai. These findings show that gridded inputs alone do not justify ConvLSTM and that architecture choice should follow strong benchmarking across average, spatial and high-rainfall performance.

Tanmay Ghosh, Shaurabh Anand, Rakesh Gomaji Nannewar +1

National Institute of Advanced Studies, Indian Institute of Science Campus, Bengaluru, India. · School of Development, Azim Premji University, Bengaluru, India. · Complex Systems Programme, National Institute of Advanced Studies, Indian Institute of Science Campus, Bengaluru, India.

Convolutional RNNsPrecipitation ForecastingForecasting BenchmarksConvolutional LSTMSpatiotemporal ForecastingWeather Forecasting
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Jun 4, 2026cs.CR

An Improved CNN-LSTM Based Intrusion Detection System for IoT Networks

With the rapid proliferation of IoT devices, security concerns have dramatically escalated and intrusion detection systems have become critical for protecting networked environments. This paper presents an improved CNN-LSTM based intrusion detection model that combines multi-class classification, dataset integration, and temporal feature learning to enhance detection performance in IoT networks. Using network traffic data, the proposed approach is evaluated on intrusion detection tasks and achieves an accuracy of approximately 97%. Experimental results demonstrate that the model effectively detects multiple attack categories while maintaining stable training and validation performance. The integration of convolutional and recurrent neural network components enables the framework to capture both spatial and temporal characteristics of network traffic, improving overall intrusion detection capability in IoT environments.

Mohammad Tariq Ikhlas, Pohanyar Khowaja Khil, Malik Muhammad Mueed Aslam +1

SEECS NUST, Pakistan

Network Intrusion DetectionInternet of ThingsConvolutional LSTM
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Jun 4, 2026cs.CR

Hybrid CNN-LSTM Framework for Intelligent Cyber Attack Detection and Prevention in U.S. Critical Digital Infrastructure: A Comparative Machine Learning Evaluation on CSE-CIC-IDS2018

Digital infrastructure is growing at a rapid pace in the United States, and as a result, exposure to advanced cyber threats to critical sectors including healthcare, finance, transportation, energy and government systems is growing. The traditional cybersecurity approaches, including signature-based intrusion detection systems, have become less effective against today's cyber attacks, as they are unable to detect unknown and changing attacks in real time. To overcome these constraints, this research suggests a smart cyber-defense system, which utilizes Artificial Intelligence (AI) and Machine Learning (ML) algorithms in the detection and prevention of cyber attacks in the U.S. digital infrastructure. This study uses the CSE-CIC-IDS2018 dataset, which is a realistic network traffic dataset, along with various cyber attack scenarios, including Distributed Denial of Service (DDoS), brute force attacks, botnets, infiltration attacks, and web-based attacks. A number of machine learning and deep learning models such as Random Forest, XGBoost, Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks are implemented and evaluated to be used in identifying malicious network behavior and boosting the accuracy of intrusion detection. The framework proposed combines data preprocessing, feature engineering, real-time traffic monitoring, intelligent threat classification with automated prevention mechanisms to build cybersecurity resilience. E

Md. Iqbal Hossan, Md. Serajul Kabir Chowdhury Rubel, Md. Arifur Rahman +1

Department of Computer Science, Maharishi International University, Fairfield, Iowa, USA. · Department of Information Studies, Trine University, Angola, Indiana, USA. · Department of Business Information Systems, Central Michigan University, Mount Pleasant, Michigan, USA.

Autonomous Cyber DefenseNetwork Intrusion DetectionCybersecurityConvolutional LSTM
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May 21, 2026astro-ph.HE

Self-Supervised ConvLSTM for Fermi Large Area Telescope Transient Detection

We present a framework for detecting transient gamma-ray phenomena in a controlled environment by combining end-to-end simulations of the Fermi-LAT sky with self-supervised spatio-temporal deep learning. We generate a ten-year synthetic Universe with gtobssim and process the simulated events into daily all-sky maps of counts and exposure, obtaining a time-ordered sequence that mirrors the structure of Fermi-LAT observations. To model the nominal evolution of the sky, we employ a Convolutional Long Short-Term Memory (ConvLSTM) network that operates directly on map sequences, preserving spatial locality while learning temporal dependencies. The model is trained to reconstruct expected emission, and departures from the learned baseline are quantified through pixel-wise mean-squared residual maps. We then define statistically motivated anomaly criteria by estimating per-pixel thresholds from the residual distribution on the training set, and we enforce spatial coherence via local filtering to suppress isolated fluctuations. The ConvLSTM is then deployed as trained predictor on Fermi-LAT daily maps, where the sky can depart from the nominal behavior because of genuine astrophysical variability and instrumental non-stationarities. The resulting pipeline flags localized, time-dependent excesses consistent with high-variable sources or transient events (e.g., flares or GRBs) and provides a benchmark for evaluating anomaly-detection strategies on long-duration, Fermi-LAT-like datasets.

Alberto Garinei, Stefano Speziali, Alessandro Vispa +11

Idea-Re S.r.l., 06125, Perugia, Italy · bDipartimento Scienze Ingegneristiche, Università Guglielmo Marconi, 00193, Rome, Italy · cIstituto Nazionale Fisica Nucleare, sezione di Perugia, 06123, Perugia, Italy +1

Convolutional LSTMHigh-Energy PhysicsReconstruction-Based Anomaly DetectionTime-Series Anomaly Detection
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May 14, 2026cs.LG

GPU-Accelerated Deep Learning for Heatwave Prediction and Urban Heat Risk Assessment

Heatwaves are an important problem in cities, and climate change makes this problem more difficult. In this paper, we present a GPU-based deep learning framework for next-day prediction of urban thermal conditions and for heat risk assessment. The study was carried out in Sarajevo by using MODIS land surface temperature data and Open-Meteo forecast data. We tested several models, including convolutional models and spatiotemporal models. Among them, ConvLSTM with a mixed loss function gave the best results. The obtained values were MAE = 0.2293, RMSE = 0.3089, and R2 = 0.8877. The experiments also showed that results can be improved by using longer temporal series and additional meteorological variables. Since the framework was implemented on a GPU and trained with mixed precision, the execution time was reduced. Based on the predicted temperature fields, it was also possible to combine hazard information with exposure and vulnerability data in order to generate city heat risk maps. The proposed framework can be used as a practical basis for city heat analysis.

Adis Alihodžić

Department of Mathematical and Computer Sciences, Faculty of Science, University of Sarajevo, Sarajevo, Bosnia and Herzegovina.

Convolutional LSTMSpatiotemporal ForecastingWeather Forecasting
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May 13, 2026cs.LG

Spatiotemporal downscaling and nowcasting of urban land surface temperatures with deep neural networks

Land Surface Temperature (LST) is a key variable for various applications, such as urban climate and ecology studies. Yet, existing satellite-derived LST products provide either high spatial or high temporal resolution, resulting in a fundamental trade-off between the two. To address this trade-off, we combine observations from a geostationary and a polar orbiting satellite and provide LST fields at high spatial and high temporal resolution (1 km at 15-min intervals). We demonstrate their application for intraday forecasting of LSTs. To estimate LST fields at high spatiotemporal resolution, a U-Net model is trained to map LST fields from SEVIRI/MSG (3 km and 15 min resolution) to LST fields from Terra/Aqua MODIS (1 km, 4 overpasses per day) that are collocated in space and time. The presented model has been trained on LSTs across large European cities with a population exceeding 1 million inhabitants, and achieves an RMSE = 1.921.92°C and near-zero bias MBE = 0.010.01°C on the hold-out test set. As a second step, we present an LST nowcasting model based on ConvLSTM architecture, trained across downscaled LST fields with forecast lead times of 15 to 75 minutes. The nowcasting model outperforms a persistence and a Climatological Rolling Median benchmarks, with RMSEs of 0.570.57 to 1.151.15°C for the considered lead times and biases ranging from −0.1-0.1 to 0.140.14°C. An additional validation conducted against independent MODIS overpasses confirms robust performance. Our LST forecast model at high spatiotemporal resolution is directly applicable to operational satellite-based LST monitoring.

Solomiia Kurchaba, Angela Meyer

Department of Geoscience and Remote Sensing · School of Engineering and Computer Science, Bern University of Applied Sciences

U-NetConvolutional LSTMRemote SensingSpatiotemporal Forecasting
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Apr 17, 2026cs.CV

Multilevel neural networks with dual-stage feature fusion for human activity recognition

Human activity recognition (HAR) refers to the process of identifying human actions and activities using data collected from sensors. Neural networks, such as convolutional neural networks (CNNs), long short-term memory (LSTM) networks, convolutional LSTM, and their hybrid combinations, have demonstrated exceptional performance in various research domains. Developing a multilevel individual or hybrid model for HAR involves strategically integrating multiple networks to capitalize on their complementary strengths. The structural arrangement of these components is a critical factor influencing the overall performance. This study explores a novel framework of a two-level network architecture with dual-stage feature fusion: late fusion, which combines the outputs from the first network level, and intermediate fusion, which integrates the features from both the first and second levels. We evaluated 1515 different network architectures of CNNs, LSTMs, and convolutional LSTMs, incorporating late fusion with and without intermediate fusion, to identify the optimal configuration. Experimental evaluation on two public benchmark datasets demonstrates that architectures incorporating both late and intermediate fusion achieve higher accuracy than those relying on late fusion alone. Moreover, the optimal configuration outperforms baseline models, thereby validating its effectiveness for HAR.

Abeer FathAllah Brery, Ascensión Gallardo-Antolín, Israel Gonzalez-Carrasco +1

Human Activity RecognitionRecurrent Neural NetworksConvolutional LSTMConvolutional Neural NetworksFeature Fusion
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