Results 181 to 190 of about 197,676 (281)

Measurement-based adaptation protocol with quantum reinforcement learning [PDF]

, 2018
F. Albarrán-Arriagada, J. C. Retamal, E. Solano, Lucas Lamata   +3 more
openalex   +1 more source

Advanced Ceramics in Aerospace and Defense: The Interrelationship between Traditional and Additive Manufacturing Approaches

Advanced Materials Technologies, EarlyView.
Additive manufacturing revolutionizes production by enabling on‐demand, customized, and sustainable manufacturing with streamlined supply chains. While metal and polymer AM are well‐established, advanced ceramic AM is rapidly emerging, overcoming traditional material challenges.
Kateryna Oleksandrivna Shvydyuk, Senentxu Lanceros‐Mendez, Abílio Pereira Silva   +2 more
wiley   +1 more source

Discovering state-of-the-art reinforcement learning algorithms. [PDF]

Nature
Oh J, Farquhar G, Kemaev I, Calian DA, Hessel M, Zintgraf L, Singh S, van Hasselt H, Silver D.   +8 more
europepmc   +1 more source

Deep Reinforcement Learning for Autonomous Driving Systems [PDF]

Sanskar Jadhav -, Vedant Sonwalkar -, Shweta Shewale -, Pranav Shitole -, Samarth Bhujadi -   +4 more
openalex   +1 more source

Advanced Techniques for Scalable Woven E‐Textiles Manufacturing

Advanced Materials Technologies, EarlyView.
This review highlights recent advances in scalable weaving techniques for e‐textiles, emphasizing innovations in multilayer structures, conductive yarn integration, and loom modifications. It summarizes emerging materials, fabrication strategies, and performance considerations that enable reliable, durable, and industrially scalable woven electronic ...
Faisal Abedin, Seonyoung Youn, Amanda C. Mills   +2 more
wiley   +1 more source

Reinforcement Learning-Based Intelligent Path Planning for Optimal Navigation in Dynamic Environments. [PDF]

Neural Process Lett
Yadav AK, Sharma P, Cheng X, Shukla SSP.
europepmc   +1 more source

Strategies for Enhancing Thermal Conductivity of PDMS in Electronic Applications

Advanced Materials Technologies, EarlyView.
This review explores effective strategies for enhancing heat dissipation in Polydimethylsiloxane (PDMS)‐based composites, focusing on particle optimization, 3D network design, and multifunctional integration. It offers key insights into cutting‐edge methods and simulations that are advancing thermal management in modern electronic devices.
Xiang Yan, Marisol Martin‐Gonzalez
wiley   +1 more source

SINDy-RL for interpretable and efficient model-based reinforcement learning. [PDF]

Nat Commun
Zolman N, Lagemann C, Fasel U, Kutz JN, Brunton SL.   +4 more
europepmc   +1 more source

A Deep Reinforcement Learning based Scheduler for IoT Devices in Co-existence with 5G-NR [PDF]

Shahida Jabeen
openalex   +1 more source

Characterization of Droplet Formation in Ultrasonic Spray Coating: Influence of Ink Formulation Using Phase Doppler Anemometry and Machine Learning

Advanced Materials Technologies, EarlyView.
This study explores how machine learning models, trained on small experimental datasets obtained via Phase Doppler Anemometry (PDA), can accurately predict droplet size (D32) in ultrasonic spray coating (USSC). By capturing the influence of ink complexity (solvent, polymer, nanoparticles), power, and flow rate, the model enables precise droplet control
Pieter Verding, Danny E. P. Vanpoucke, Yunus T. Aksoy, Tobias Corthouts, Maria R. Vetrano, Wim Deferme   +5 more
wiley   +1 more source