Výsledky vyhledávání - (dynamical OR dynamika) system autoencoder*

Context-Dependent Manifold Learning in Dynamical Systems: A Neuromodulated Constrained Autoencoder Approach

Autoři: Adriaens, Jérôme Drion, Guillaume Sacré, Pierre

Zdroj: Symmetry and Geometry in Neural Representations workshop, NeurIPS 2025, San Diego, United States - California [US-CA], Tuesday Dec 2nd through Sunday Dec 7th

Témata: Nonlinear dimensionality reduction, Neuromodulation, Context-dependent learning, Constrained autoencoder, Engineering, computing & technology, Ingénierie, informatique & technologie

Přístupová URL adresa: https://orbi.uliege.be/handle/2268/336469

Sensor-Driven Surrogate Modeling and Control of Nonlinear Dynamical Systems Using FAE-CAE-LSTM and Deep Reinforcement Learning

Autoři: Kherad, M. Moayyedi, M. K. Fotouhi-Ghazvini, F. a další

Zdroj: Sensors. 25(16)

Témata: Autoencoders, Control Of Dynamical Systems, Cyber-physical Systems, Deep Reinforcement Learning, Long Short-term Memory, Sensor-driven Modeling, Circular Cylinders, Computer Control Systems, Convolution, Dynamical Systems, Dynamics, Embedded Systems, Flow Control, Fuel Additives, Nonlinear Dynamical Systems, Real Time Control, Robust Control, Auto Encoders, Control Of Dynamical System, Cybe-physical Systems, High-dimensional, Higher-dimensional, Reinforcement Learning Agent, Reinforcement Learnings, Short Term Memory

Popis souboru: print

Přístupová URL adresa: https://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-73172
https://doi.org/10.3390/s25165149

Stochastic and Non-local Closure Modeling for Nonlinear Dynamical Systems via Latent Score-based Generative Models

Autoři: Dong, Xinghao Yang, Huchen Wu, Jin-Long

Témata: FOS: Computer and information sciences, FOS: Mathematics, FOS: Physical sciences, Dynamical Systems (math.DS), Computational Physics (physics.comp-ph), Machine Learning (cs.LG)

On Ensemble Learning Models for Autoencoder-Based Identification of Nonlinear Dynamical Systems

Autoři: Nicola Arridu Martina Lippi Mauro Franceschelli a další

Zdroj: IEEE Access. 13:169071-169082

Témata: autoencoders, data-driven analysis, ensemble learning, Nonlinear system identification

Přístupová URL adresa: https://hdl.handle.net/11590/521097
https://doi.org/10.1109/ACCESS.2025.3611914

Hybrid Meta-Learning Framework for Anomaly Forecasting in Nonlinear Dynamical Systems via Physics-Inspired Simulation and Deep Ensembles

Autoři: Bereketoglu, Abdullah Burkan

Témata: Accelerator Physics (physics.acc-ph), FOS: Computer and information sciences, FOS: Electrical engineering, electronic engineering, information engineering, FOS: Physical sciences, Systems and Control (eess.SY), Machine Learning (cs.LG)

Dynamical System Autoencoders

Autoři: He, Shiquan Paffenroth, Randy Cava, Olivia a další

Zdroj: 2024 International Conference on Machine Learning and Applications (ICMLA) ; page 1496-1503

Dostupnost: https://doi.org/10.1109/icmla61862.2024.00231
http://xplorestaging.ieee.org/ielx8/10903208/10903212/10903329.pdf?arnumber=10903329

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

Autoři: Vijayamanikandan Vijayarangan Harshavardhana A. Uranakara Francisco E. Hernández–Pérez a další

Zdroj: Energy and AI, Vol 21, Iss , Pp 100567- (2025)

Témata: Neural ODE, Autoencoders, Information bottleneck theory, Dynamical systems, Disentanglement, Electrical engineering. Electronics. Nuclear engineering, TK1-9971, Computer software, QA76.75-76.765

Popis souboru: electronic resource

Relation: http://www.sciencedirect.com/science/article/pii/S2666546825000990; https://doaj.org/toc/2666-5468

Přístupová URL adresa: https://doaj.org/article/0078f652db91496fbf58605196636ba7

Temporally-consistent koopman autoencoders for forecasting dynamical systems

Autoři: Indranil Nayak Ananda Chakrabarti Mrinal Kumar a další

Zdroj: Scientific Reports, Vol 15, Iss 1, Pp 1-13 (2025)

Témata: Koopman, Machine learning, Neural networks, Medicine, Science

Popis souboru: electronic resource

Relation: https://doaj.org/toc/2045-2322

Přístupová URL adresa: https://doaj.org/article/ddea48af645c49b99d39e1b25ee501e8

Adaptive path-integral autoencoder: representation learning and planning for dynamical systems

Autoři: Ha, Jung-Su Park, Young-Jin Chae, Hyeok-Joo a další

Zdroj: Journal of Statistical Mechanics: Theory and Experiment ; volume 2019, issue 12, page 124008 ; ISSN 1742-5468

Dostupnost: https://doi.org/10.1088/1742-5468/ab3455
https://iopscience.iop.org/article/10.1088/1742-5468/ab3455/pdf
https://iopscience.iop.org/article/10.1088/1742-5468/ab3455

Transfer Learning for Dynamical Systems Models via Autoencoders and GANs

Autoři: Angelo Damiani Gustavo Viera Lopez Giorgio Manganini a další

Zdroj: 2024 American Control Conference (ACC). :8-14

Slim multi-scale convolutional autoencoder-based reduced-order models for interpretable features of a complex dynamical system

Autoři: Teutsch, Philipp Pfeffer, Philipp Ghazijahani, Mohammad Sharifi a další

Témata: Physics - Fluid Dynamics, Computer Science - Machine Learning, text

URL: http://arxiv.org/abs/2501.03070

Data-driven identification of nonlinear dynamical systems with LSTM autoencoders and Normalizing Flows

Autoři: Rostamijavanani, Abdolvahhab Li, Shanwu Yang, Yongchao

Témata: Computer Science - Machine Learning, text

URL: http://arxiv.org/abs/2503.03977

Slim multi-scale convolutional autoencoder-based reduced-order models for interpretable features of a complex dynamical system

Autoři: Philipp Teutsch Philipp Pfeffer Mohammad Sharifi Ghazijahani a další

Zdroj: APL Machine Learning, Vol 3, Iss 1, Pp 016112-016112-15 (2025)

Témata: FOS: Computer and information sciences, Computer Science - Machine Learning, Physics, QC1-999, Electronic computers. Computer science, Fluid Dynamics (physics.flu-dyn), FOS: Physical sciences, QA75.5-76.95, Physics - Fluid Dynamics, Machine Learning (cs.LG)

Přístupová URL adresa: http://arxiv.org/abs/2501.03070
https://doaj.org/article/236f8e1f2be64001a1d9c1d7b38ae1f2

MODELING UNKNOWN STOCHASTIC DYNAMICAL SYSTEM VIA AUTOENCODER

Autoři: Xu, Zhongshu Chen, Yuan Chen, Qifan a další

Zdroj: Journal of Machine Learning for Modeling and Computing. 5:87-112

Témata: FOS: Computer and information sciences, Computer Science - Machine Learning, Statistics - Machine Learning, FOS: Mathematics, Machine Learning (stat.ML), Mathematics - Numerical Analysis, Numerical Analysis (math.NA), 60H10, 60H35, 62M45, 65C30, Machine Learning (cs.LG)

Přístupová URL adresa: http://arxiv.org/abs/2312.10001

Inferring the time-varying coupling of dynamical systems with temporal convolutional autoencoders

Autoři: Calderon, Josuan Berman, Gordon J.

Témata: FOS: Computer and information sciences, 0301 basic medicine, Computer Science - Machine Learning, 0303 health sciences, 03 medical and health sciences, FOS: Biological sciences, Quantitative Biology - Quantitative Methods, Quantitative Methods (q-bio.QM), Machine Learning (cs.LG)

Přístupová URL adresa: http://arxiv.org/abs/2406.03212

Dynamical system autoencoders using piecewise linear transformation

Autoři: Shiquan He Randy Paffenroth

Zdroj: Seventh International Conference on Image, Video Processing, and Artificial Intelligence (IVPAI 2025). :24

Learning nonlinear projections for reduced-order modeling of dynamical systems using constrained autoencoders

Autoři: Samuel E. Otto Gregory R. Macchio Clarence W. Rowley

Zdroj: Chaos: An Interdisciplinary Journal of Nonlinear Science. 33

Témata: Mathematics - Differential Geometry, FOS: Computer and information sciences, Computer Science - Machine Learning, Dynamical Systems (math.DS), Systems and Control (eess.SY), 68T07, 34A34, 34C20, 49Q12, 53Z30, 53Z50, 14M15, 65K10, 46N10, 37C86, Electrical Engineering and Systems Science - Systems and Control, Machine Learning (cs.LG), Computational Engineering, Finance, and Science (cs.CE), Differential Geometry (math.DG), FOS: Mathematics, FOS: Electrical engineering, electronic engineering, information engineering, Mathematics - Dynamical Systems, Computer Science - Computational Engineering, Finance, and Science

Přístupová URL adresa: https://pubmed.ncbi.nlm.nih.gov/38011714
http://arxiv.org/abs/2307.15288

Adaptive Koopman embedding for robust control of nonlinear dynamical systems.

Autoři: Singh, Rajpal Sah, Chandan Kumar Keshavan, Jishnu

Zdroj: International Journal of Robotics Research. Nov2025, Vol. 44 Issue 13, p2235-2261. 27p.

Témata: *ROBUST control, *NONLINEAR dynamical systems, *TRACKING control systems, *GENERALIZATION, *PREDICTIVE control systems, *ARTIFICIAL neural networks, *EMPIRICAL research, *COMPOSITION operators

Deep Variational Bayesian Approach for Sensor‐Driven Modeling and Monitoring of Dynamic Degrading Systems.

Autoři: Tanzeem, Nadia Moghaddass, Ramin

Zdroj: Quality & Reliability Engineering International; Oct2025, Vol. 41 Issue 6, p2346-2372, 27p

Témata: RECURRENT neural networks, PROBABILISTIC generative models, DYNAMICAL systems, LATENT variables, AUTOENCODERS, FAULT diagnosis

Temporally-Consistent Koopman Autoencoders for Forecasting Dynamical Systems

Autoři: Nayak, Indranil Chakrabarty, Ananda Kumar, Mrinal a další

Témata: Computer Science - Machine Learning, text

URL: http://arxiv.org/abs/2403.12335