Cargando…

AI Pontryagin or how artificial neural networks learn to control dynamical systems

The efficient control of complex dynamical systems has many applications in the natural and applied sciences. In most real-world control problems, both control energy and cost constraints play a significant role. Although such optimal control problems can be formulated within the framework of variat...

Descripción completa

Detalles Bibliográficos
Autores principales:	Böttcher, Lucas, Antulov-Fantulin, Nino, Asikis, Thomas
Formato:	Online Artículo Texto
Lenguaje:	English
Publicado:	Nature Publishing Group UK 2022
Materias:	Article
Acceso en línea:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8763915/ https://www.ncbi.nlm.nih.gov/pubmed/35039488 http://dx.doi.org/10.1038/s41467-021-27590-0

Ejemplares similares

On the accuracy of short-term COVID-19 fatality forecasts
por: Antulov-Fantulin, Nino, et al.
Publicado: (2022)

Pontryagin Conference
por: Collective
Publicado: (1998)

Simulating SIR processes on networks using weighted shortest paths
por: Tolić, Dijana, et al.
Publicado: (2018)

Generalized network dismantling
por: Ren, Xiao-Long, et al.
Publicado: (2019)

On some fundamental challenges in monitoring epidemics
por: Vasiliauskaite, Vaiva, et al.
Publicado: (2022)

Virtual Sensoring of Motion Using Pontryagin’s Treatment of Hamiltonian Systems
por: Sands, Timothy
Publicado: (2021)

Pontryagin duality and the structure of locally compact Abelian group
por: Morris, Sidney A
Publicado: (1977)

On the impact of publicly available news and information transfer to financial markets
por: Jazbec, Metod, et al.
Publicado: (2021)

Schur functions, operator colligations, and reproducing kernel pontryagin spaces
por: Alpay, Daniel, et al.
Publicado: (1997)

The Pontryagin duality of compact o-dimensional semilattices and its applications
por: Hofmann, Karl Heinrich, et al.
Publicado: (1974)

The canonical equation of adaptive dynamics for life histories: from fitness-returns to selection gradients and Pontryagin’s maximum principle
por: Metz, Johan A. Jacob, et al.
Publicado: (2015)

Cohesiveness in Financial News and its Relation to Market Volatility
por: Piškorec, Matija, et al.
Publicado: (2014)

General Pontryagin-type stochastic maximum principle and backward stochastic evolution equations in infinite dimensions
por: Lü, Qi, et al.
Publicado: (2014)

A Pontryagin’s maximum principle and optimal control model with cost-effectiveness analysis of the COVID-19 epidemic
por: Fatmawati, et al.
Publicado: (2023)

Dynamic Artificial Neural Networks with Affective Systems
por: Schuman, Catherine D., et al.
Publicado: (2013)

How value-sensitive design can empower sustainable consumption
por: Asikis, Thomas, et al.
Publicado: (2021)

Delay learning in artificial neural networks
por: Myers, Catherine E
Publicado: (1992)

Artificial intelligence for fashion: how AI is revolutionizing the fashion industry
por: Luce, Leanne
Publicado: (2019)

Artificial intelligence for fashion: how ai is revolutionizing the fashion industry
por: Luce, Leanne
Publicado: (2018)

How can artificial neural networks approximate the brain?
por: Shao, Feng, et al.
Publicado: (2023)

Artificial intelligence (AI) and global health: how can AI contribute to health in resource-poor settings?
por: Wahl, Brian, et al.
Publicado: (2018)

Artificial Intelligence Business: How you can profit from AI
por: Chojecki, Przemek
Publicado: (2020)

AI in healthcare: how artificial intelligence is changing IT operations and infrastructure services
por: Shimonski, Robert
Publicado: (2021)

The deep learning AI playbook: strategy for disruptive artificial intelligence
por: Perez, Carlos E
Publicado: (2017)

Intelligent Control with Artificial Neural Networks for Automated Insulin Delivery Systems
por: de Farias, João Lucas Correia Barbosa, et al.
Publicado: (2022)

Non-smooth Bayesian learning for artificial neural networks
por: Fakhfakh, Mohamed, et al.
Publicado: (2022)

Artificial psychology: psychological modeling and testing of AI systems
por: Crowder, James A, et al.
Publicado: (2019)

Aedes-AI: Neural network models of mosquito abundance
por: Kinney, Adrienne C., et al.
Publicado: (2021)

Responsible artificial intelligence: how to develop and use AI in a responsible way
por: Dignum, Virginia
Publicado: (2019)

Artificial intelligence revolution: how AI will change our society, economy, and culture
por: Li, Robin, et al.
Publicado: (2020)

Matlab deep learning: with machine learning, neural networks and artificial intelligence
por: Kim, Phil
Publicado: (2017)

Editorial: Combined artificial intelligence and molecular dynamics (AI-MD) methods
por: Zhang, Leili, et al.
Publicado: (2022)

Learning from imbalanced fetal outcomes of systemic lupus erythematosus in artificial neural networks
por: Ma, Jing-Hang, et al.
Publicado: (2021)

Artificial neural networks
por: Cain, Gayle
Publicado: (2016)

A Model for Improving the Learning Curves of Artificial Neural Networks
por: Monteiro, Roberto L. S., et al.
Publicado: (2016)

Brain-inspired replay for continual learning with artificial neural networks
por: van de Ven, Gido M., et al.
Publicado: (2020)

The executive guide to artificial intelligence: how to identify and implement applications for AI in your organization
por: Burgess, Andrew
Publicado: (2017)

Artificial intelligence across industries: how AI is transforming telco, retail, and financial services
por: Barlow, Mike
Publicado: (2017)

Automated Lung Cancer Detection Using Artificial Intelligence (AI) Deep Convolutional Neural Networks: A Narrative Literature Review
por: Sathyakumar, Kaviya, et al.
Publicado: (2020)

The AI organization: learn from real companies and Microsoft's journey how to redefine your organization with AI
por: Carmona, David
Publicado: (2019)

Cannot write session to /tmp/vufind_sessions/sess_2uakukgit29q8t47u7q5im5qqm