Suchergebnisse - extrapolative adversarial variational autoencoder*

Controllable Blind AC FDIA via Physics-Informed Extrapolative AVAE

Autoren: Siliang Zhao Wuman Luo Qin Shu et al.

Quelle: Sensors ; Volume 25 ; Issue 3 ; Pages: 943

Schlagwörter: controllable false data injection attack, AC state estimation, physics informed, data driven, extrapolative adversarial variational autoencoder

Dateibeschreibung: application/pdf

Relation: Sensor Networks; https://dx.doi.org/10.3390/s25030943

Verfügbarkeit: https://doi.org/10.3390/s25030943

Controllable Blind AC FDIA via Physics-Informed Extrapolative AVAE.

Autoren: Zhao, Siliang Luo, Wuman Shu, Qin et al.

Quelle: Sensors (14248220); Feb2025, Vol. 25 Issue 3, p943, 26p

Schlagwörter: PHYSICAL laws, AUTOENCODERS, ELECTRICAL load, DATA distribution, ELECTRIC potential measurement

A fast transonic airfoil flow field prediction model based on a modified Fourier neural operator.

Autoren: Tang, Weishao Wu, Chenyu Yang, Yunjia et al.

Quelle: SCIENCE CHINA Physics, Mechanics & Astronomy; Jan2026, Vol. 69 Issue 1, p1-18, 18p

Deep convolutional architectures for extrapolative forecasts in time-dependent flow problems.

Autoren: Bhatt, Pratyush Kumar, Yash Soulaïmani, Azzeddine

Quelle: Advanced Modeling & Simulation in Engineering Sciences; 11/30/2023, Vol. 10 Issue 1, p1-35, 35p

DFT and hybrid classical–quantum machine learning integration for photocatalyst discovery and hydrogen production.

Autoren: Wayo, Dennis Delali Kwesi Goliatt, Leonardo Ganji, Masoud Darvish

Quelle: Reviews in Chemical Engineering; Nov2025, Vol. 41 Issue 8, p741-774, 34p

Schlagwörter: HYDROGEN production, PHOTOCATALYSIS, CLEAN energy, DENSITY functional theory, SEMICONDUCTORS, QUANTUM efficiency, MATERIALS science, MACHINE learning

Predictive Autonomy for UAV Remote Sensing: A Survey of Video Prediction.

Autoren: Chen, Zhan Zhu, Enze Guo, Zile et al.

Quelle: Remote Sensing; Oct2025, Vol. 17 Issue 20, p3423, 35p

Schlagwörter: REMOTE sensing, STATE-space methods, AUTONOMOUS robots, PREDICTION models, GEOSPATIAL data, RESOURCE allocation, DRONE aircraft, DYNAMIC models

Applications of Computational Mechanics Methods Combined with Machine Learning and Neural Networks: A Systematic Review (2015–2025).

Autoren: Pawlik, Lukasz Wilk-Jakubowski, Jacek Lukasz Frej, Damian et al.

Quelle: Applied Sciences (2076-3417); Oct2025, Vol. 15 Issue 19, p10816, 53p

Schlagwörter: MACHINE learning, ARTIFICIAL neural networks, COMPUTATIONAL mechanics, REDUCED-order models, UNCERTAINTY (Information theory), FINITE element method

Data-Driven Optimization of Discontinuous and Continuous Fiber Composite Processes Using Machine Learning: A Review.

Autoren: Malashin, Ivan Martysyuk, Dmitry Tynchenko, Vadim et al.

Quelle: Polymers (20734360); Sep2025, Vol. 17 Issue 18, p2557, 35p

Schlagwörter: MACHINE learning, FIBROUS composites, ARTIFICIAL intelligence, SCALABILITY, PREDICTION models, PROCESS optimization, FAULT diagnosis, QUALITY assurance

Hydrogen Energy Storage via Carbon-Based Materials: From Traditional Sorbents to Emerging Architecture Engineering and AI-Driven Optimization.

Autoren: Fu, Han Mojiri, Amin Wang, Junli et al.

Quelle: Energies (19961073); Aug2025, Vol. 18 Issue 15, p3958, 36p

Schlagwörter: CARBON-based materials, ENERGY storage, ARCHITECTURAL designs, HYDROGEN as fuel, STORAGE, CLEAN energy, SORBENTS, OPTIMIZATION algorithms

Improving the generative performance of chemical autoencoders through transfer learning.

Autoren: Iovanac, Nicolae C. Savoie, Brett M.

Quelle: Machine Learning: Science & Techonology; Dec2020, Vol. 1 Issue 4, p1-9, 9p

Deep generative abnormal lesion emphasization validated by nine radiologists and 1000 chest X-rays with lung nodules.

Autoren: Hanaoka, Shouhei Nomura, Yukihiro Hayashi, Naoto et al.

Quelle: PLoS ONE; 12/12/2024, Vol. 19 Issue 12, p1-21, 21p

Schlagwörter: CHEST X rays, IMAGE analysis, COMPUTED tomography, PULMONARY nodules, LUNG cancer

Digitized material design and performance prediction driven by high-throughput computing.

Autoren: Li, Hanhui Yang, Jiao Yao, Jingxu et al.

Quelle: Frontiers in Materials; 2025, p1-18, 18p

Schlagwörter: MACHINE learning, HIGH performance computing, FORECASTING, GENERATIVE artificial intelligence, PERFORMANCE-based design, COMPUTER simulation

A Comprehensive Guide to Interpretable AI-Powered Discoveries in Astronomy.

Autoren: Lieu, Maggie

Quelle: Universe (2218-1997); Jun2025, Vol. 11 Issue 6, p187-N.PAG, 31p

Schlagwörter: MACHINE learning, ARTIFICIAL intelligence, SCIENTIFIC discoveries, ASTRONOMY, FORECASTING

Optimized Bi-LSTM Networks for Modeling Ni(II) Biosorption Kinetics on Quercus crassipes Acorn Shells.

Autoren: Cruz-Victoria, Juan Crescenciano Aranda-García, Erick Cristiani-Urbina, Eliseo et al.

Quelle: Processes; Apr2025, Vol. 13 Issue 4, p1076, 29p

Schlagwörter: LONG short-term memory, HEAVY metal toxicology, ANALYSIS of heavy metals, BIOSORPTION, ACORNS

Interaction Mix and Match: Synthesizing Close Interaction using Conditional Hierarchical GAN with Multi‐Hot Class Embedding.

Autoren: Goel, Aman Men, Qianhui Ho, Edmond S. L.

Quelle: Computer Graphics Forum; Dec2022, Vol. 41 Issue 8, p327-338, 12p, 2 Color Photographs, 5 Diagrams, 6 Charts, 1 Graph

Schlagwörter: GENERATIVE adversarial networks, MOTION capture (Cinematography), MACHINE learning

RETRACTED: Revolutionizing power grid loss prediction with advanced hybrid time series deep learning model.

Autoren: Senthamil Selvi, M. Senthamizh Selvi, R. Subbaiyan, Saranya et al.

Quelle: Journal of Intelligent & Fuzzy Systems; 2025Supplement3, Vol. 48, p277-286, 10p

Schlagwörter: RETRACTION of scholarly articles, ELECTRIC power distribution grids, TIME series analysis

Category-Theoretical and Topos-Theoretical Frameworks in Machine Learning: A Survey.

Autoren: Jia, Yiyang Peng, Guohong Yang, Zheng et al.

Quelle: Axioms (2075-1680); Mar2025, Vol. 14 Issue 3, p204, 59p

Schlagwörter: HOMOLOGY theory, MACHINE learning, PROBABILITY theory, LOGIC, SEMANTICS

Machine Learning Approaches in Polymer Science: Progress and Fundamental for a New Paradigm.

Autoren: Xie, Chunhui Qiu, Haoke Liu, Lu et al.

Quelle: SmartMat; Feb2025, Vol. 6 Issue 1, p1-41, 41p

Schlagwörter: MACHINE learning, SUSTAINABLE design, SCIENTIFIC discoveries, SUSTAINABLE development, BIG data

An extensive review of hyperspectral image classification and prediction: techniques and challenges.

Autoren: Tejasree, Ganji Agilandeeswari, Loganathan

Quelle: Multimedia Tools & Applications; Oct2024, Vol. 83 Issue 34, p80941-81038, 98p

Schlagwörter: IMAGE recognition (Computer vision), SURFACE of the earth, FEATURE extraction, IMAGE processing, LAND cover

Applications of Long Short-Term Memory (LSTM) Networks in Polymeric Sciences: A Review.

Autoren: Malashin, Ivan Tynchenko, Vadim Gantimurov, Andrei et al.

Quelle: Polymers (20734360); Sep2024, Vol. 16 Issue 18, p2607, 44p

Schlagwörter: LONG short-term memory, POLYMER networks, MOLECULAR structure, DATA analytics, POLYMERS