Results 101 to 110 of about 3,700 (193)

NN-AE-VQE: Neural network parameter prediction on autoencoded variational quantum eigensolvers

A longstanding computational challenge is the accurate simulation of many-body particle systems. Especially for deriving key characteristics of high-impact but complex systems such as battery materials and high entropy alloys (HEA). While simple models allow for simulations of the required scale, these methods often fail to capture the complex dynamics
Mesman, Koen, Tang, Yinglu, Moller, Matthias, Chen, Boyang, Feld, Sebastian   +4 more
openaire   +2 more sources

Method for Detecting Disorder of a Nonlinear Dynamic Plant

Sensors
This paper proposes a new disorder detection method CCF-AE for a scalar dynamic plant based only on its input–output relation using a cross-correlation function and neural network autoencoder.
Xuechun Wang, Vladimir Eliseev
doaj   +1 more source

Small-data Reduced Order Modeling of Chaotic Dynamics through SyCo-AE: Synthetically Constrained Autoencoders [PDF]

, 2023
Andrey A. Popov, Renato Zanetti
openalex   +1 more source

RPG-AE: Neuro-Symbolic Graph Autoencoders with Rare Pattern Mining for Provenance-Based Anomaly Detection [PDF]

Asif Tauhid, Sidahmed Benabderrahmane, Mohamad Altrabulsi, Ahamed Foisal, Talal Rahwan   +4 more
openalex   +2 more sources

Network Intrusion Detection System Based Security System for Cloud Services Using Novel Recurrent Neural Network - Autoencoder (NRNN-AE) and Genetic

, 2023
S. Baghavathi Priya, R.S. Ponmagal
openalex   +1 more source

Exploiting Autoencoder-Based Anomaly Detection to Enhance Cybersecurity in Power Grids

Future Internet
The evolution of smart grids has led to technological advances and a demand for more efficient and sustainable energy systems. However, the deployment of communication systems in smart grids has increased the threat of cyberattacks, which can result in ...
Fouzi Harrou, Benamar Bouyeddou, Abdelkader Dairi, Ying Sun   +3 more
doaj   +1 more source

Understanding the role of autoencoders for stiff dynamical systems using information theory

Energy and AI
Using information theory, this study provides insights into how the construction of latent space of autoencoder (AE) using deep neural network (DNN) training finds a smooth (non-stiff) low-dimensional manifold in the stiff dynamical system.
Vijayamanikandan Vijayarangan, Harshavardhana A. Uranakara, Francisco E. Hernández–Pérez, Hong G. Im   +3 more
doaj   +1 more source

Autoencoder-based conditional optimal transport generative adversarial network for medical image generation

Visual Informatics
Medical image generation has recently garnered significant interest among researchers. However, the primary generative models, such as Generative Adversarial Networks (GANs), often encounter challenges during training, including mode collapse. To address
Jun Wang, Bohan Lei, Liya Ding, Xiaoyin Xu, Xianfeng Gu, Min Zhang   +5 more
doaj   +1 more source

Processed Score Graph Dataset for the Score Autoencoder (score-ae)

Hendrik Roth
openalex   +2 more sources

CNN-AE: Convolution Neural Network combined with Autoencoder approach to detect survival chance of COVID-19 patients [PDF]

, 2021
Fahime Khozeimeh, Danial Sharifrazi, Navid Hoseini Izadi, Javad Hassannataj Joloudari, Afshin Shoeibi, Roohallah Alizadehsani, J. M. Górriz, Sadiq Hussain, Zahra Alizadeh Sani, Hossein Moosaei, Abbas Khosravi, Saeid Nahavandi, Sheikh Mohammed Shariful Islam   +12 more
openalex   +1 more source