Tootfinder

A framework for realisable data-driven active flow control using model predictive control applied to a simplified truck wake
Alberto Solera-Rico, Carlos Sanmiguel Vila, Stefano Discetti
https://arxiv.org/abs/2510.11600

A framework for realisable data-driven active flow control using model predictive control applied to a simplified truck wake
We present an efficient and realisable active flow control framework with few non-intrusive sensors. The method builds upon data-driven, reduced-order predictive models based on Long-Short-Term Memory (LSTM) networks and efficient gradient-based Model Predictive Control (MPC). The model uses only surface-mounted pressure probes to infer the wake state, and is trained entirely offline on a dataset built with open-loop actuations, thus avoiding the complexities of online learning. Sparsification …

Short-Term Regional Electricity Demand Forecasting in Argentina Using LSTM Networks
Oscar A. Oviedo
https://arxiv.org/abs/2509.19374 https://arxiv.org/pdf/…

Short-Term Regional Electricity Demand Forecasting in Argentina Using LSTM Networks
This study presents the development and optimization of a deep learning model based on Long Short-Term Memory (LSTM) networks to predict short-term hourly electricity demand in Córdoba, Argentina. Integrating historical consumption data with exogenous variables (climatic factors, temporal cycles, and demographic statistics), the model achieved high predictive precision, with a mean absolute percentage error of 3.20\% and a determination coefficient of 0.95. The inclusion of periodic temporal e…

IntrusionX: A Hybrid Convolutional-LSTM Deep Learning Framework with Squirrel Search Optimization for Network Intrusion Detection
Ahsan Farabi, Muhaiminul Rashid Shad, Israt Khandaker
https://arxiv.org/abs/2510.00572

IntrusionX: A Hybrid Convolutional-LSTM Deep Learning Framework with Squirrel Search Optimization for Network Intrusion Detection
Intrusion Detection Systems (IDS) face persistent challenges due to evolving cyberattacks, high-dimensional traffic data, and severe class imbalance in benchmark datasets such as NSL-KDD. To address these issues, we propose IntrusionX, a hybrid deep learning framework that integrates Convolutional Neural Networks (CNNs) for local feature extraction and Long Short-Term Memory (LSTM) networks for temporal modeling. The architecture is further optimized using the Squirrel Search Algorithm (SSA), e…

Diffusion-Based Probabilistic Modeling for Hourly Streamflow Prediction and Assimilation
Wencong Yang, Haoyu Ji, Leo Lonzarich, Yalan Song, Chaopeng Shen
https://arxiv.org/abs/2510.08488

Diffusion-Based Probabilistic Modeling for Hourly Streamflow Prediction and Assimilation
Hourly predictions are critical for issuing flood warnings as the flood peaks on the hourly scale can be distinctly higher than the corresponding daily ones. Currently a popular hourly data-driven prediction scheme is multi-time-scale long short-term memory (MTS-LSTM), yet such models face challenges in probabilistic forecasts or integrating observations when available. Diffusion artificial intelligence (AI) models represent a promising method to predict high-resolution information, e.g., hourl…

Identifying non-equilibrium fluctuations in Intracellular Motion Using Recurrent Neural Networks
Tomas Basile, Natascha Leijnse, Malte Slot Lauridsen, Younes Farhangi Barooji, Amin Doostmohammadi, Karel Proesmans
https://arxiv.org/abs/2510.04485

Identifying non-equilibrium fluctuations in Intracellular Motion Using Recurrent Neural Networks
Distinguishing active from passive dynamics is a fundamental challenge in understanding the motion of living cells and other active matter systems. Here, we introduce a framework that combines physical modeling, analytical theory, and machine learning to identify and characterize active fluctuations from trajectory data. We train a long short-term memory (LSTM) neural network on synthetic trajectories generated from well-defined stochastic models of active particles, enabling it to classify mot…

Physics-Informed GCN-LSTM Framework for Long-Term Forecasting of 2D and 3D Microstructure Evolution
Hamidreza Razavi, Nele Moelans
https://arxiv.org/abs/2509.15029 https://

Physics-Informed GCN-LSTM Framework for Long-Term Forecasting of 2D and 3D Microstructure Evolution
This paper presents a physics-informed framework that integrates graph convolutional networks (GCN) with long short-term memory (LSTM) architecture to forecast microstructure evolution over long time horizons in both 2D and 3D with remarkable performance across varied metrics. The proposed framework is composition-aware, trained jointly on datasets with different compositions, and operates in latent graph space, which enables the model to capture compositions and morphological dynamics while re…

Detection of kink oscillations in solar coronal loops by a CNN-LSTM neural network
Sergey A. Belov, Yu Zhong, Dmitrii Y. Kolotkov, Valery M. Nakariakov
https://arxiv.org/abs/2509.15041

Detection of kink oscillations in solar coronal loops by a CNN-LSTM neural network
A hybrid machine learning model which combines a shallow convolutional neural network and a long short-term memory network (CNN--LSTM), has been developed to automate the detection of kink oscillations in coronal plasma loops within large volumes of high-cadence sequences of imaging data. The network was trained on a set of 10,000 synthetic data cubes designed to mimic sequences of coronal images, achieving an accuracy greater than 98\% on this synthetic dataset. The model was then applied to d…

Predictive Performance of LSTM Networks on Sectoral Stocks in an Emerging Market: A Case Study of the Pakistan Stock Exchange
Ahad Yaqoob, Syed M. Abdullah
https://arxiv.org/abs/2509.14401

Predictive Performance of LSTM Networks on Sectoral Stocks in an Emerging Market: A Case Study of the Pakistan Stock Exchange
The application of deep learning models for stock price forecasting in emerging markets remains underexplored despite their potential to capture complex temporal dependencies. This study develops and evaluates a Long Short-Term Memory (LSTM) network model for predicting the closing prices of ten major stocks across diverse sectors of the Pakistan Stock Exchange (PSX). Utilizing historical OHLCV data and an extensive set of engineered technical indicators, we trained and validated the model on a…

SINAI at eRisk@CLEF 2023: Approaching Early Detection of Gambling with Natural Language Processing
Alba Maria Marmol-Romero, Flor Miriam Plaza-del-Arco, Arturo Montejo-Raez
https://arxiv.org/abs/2509.14797

SINAI at eRisk@CLEF 2023: Approaching Early Detection of Gambling with Natural Language Processing
This paper describes the participation of the SINAI team in the eRisk@CLEF lab. Specifically, one of the proposed tasks has been addressed: Task 2 on the early detection of signs of pathological gambling. The approach presented in Task 2 is based on pre-trained models from Transformers architecture with comprehensive preprocessing data and data balancing techniques. Moreover, we integrate Long-short Term Memory (LSTM) architecture with automodels from Transformers. In this Task, our team has be…

Deep Learning for Model-Free Prediction of Thermal States of Robot Joint Motors
Trung Kien La, Eric Guiffo Kaigom
https://arxiv.org/abs/2509.12739 https://…

Deep Learning for Model-Free Prediction of Thermal States of Robot Joint Motors
In this work, deep neural networks made up of multiple hidden Long Short-Term Memory (LSTM) and Feedforward layers are trained to predict the thermal behavior of the joint motors of robot manipulators. A model-free and scalable approach is adopted. It accommodates complexity and uncertainty challenges stemming from the derivation, identification, and validation of a large number of parameters of an approximation model that is hardly available. To this end, sensed joint torques are collected and…

From Headlines to Holdings: Deep Learning for Smarter Portfolio Decisions
Yun Lin, Jiawei Lou, Jinghe Zhang
https://arxiv.org/abs/2509.24144 https://arxiv.…

From Headlines to Holdings: Deep Learning for Smarter Portfolio Decisions
Deep learning offers new tools for portfolio optimization. We present an end-to-end framework that directly learns portfolio weights by combining Long Short-Term Memory (LSTM) networks to model temporal patterns, Graph Attention Networks (GAT) to capture evolving inter-stock relationships, and sentiment analysis of financial news to reflect market psychology. Unlike prior approaches, our model unifies these elements in a single pipeline that produces daily allocations. It avoids the traditional…

CWT-LSTM Autoencoder: A Novel Approach for Gravitational Wave Detection in Synthetic Data
Jericho Cain
https://arxiv.org/abs/2509.10505 https://arxiv.org/p…

CWT-LSTM Autoencoder: A Novel Approach for Gravitational Wave Detection in Synthetic Data
Gravitational wave detection requires sophisticated signal processing to identify weak astrophysical signals buried in instrumental noise. Traditional matched filtering approaches face computational challenges with diverse signal morphologies and non-stationary noise. This work presents a deep learning approach combining Continuous Wavelet Transform (CWT) preprocessing with Long Short-Term Memory (LSTM) autoencoder architecture for gravitational wave detection in synthetic data. The CWT provide…

Sustainable LSTM-Based Precoding for RIS-Aided mmWave MIMO Systems with Implicit CSI
Po-Heng Chou, Jiun-Jia Wu, Wan-Jen Huang, Ronald Y. Chang
https://arxiv.org/abs/2509.12658 h…

Sustainable LSTM-Based Precoding for RIS-Aided mmWave MIMO Systems with Implicit CSI
In this paper, we propose a sustainable long short-term memory (LSTM)-based precoding framework for reconfigurable intelligent surface (RIS)-assisted millimeter-wave (mmWave) MIMO systems. Instead of explicit channel state information (CSI) estimation, the framework exploits uplink pilot sequences to implicitly learn channel characteristics, reducing both pilot overhead and inference complexity. Practical hardware constraints are addressed by incorporating the phase-dependent amplitude model of…