Results 81 to 90 of about 298,347 (266)

Universal Electronic‐Structure Relationship Governing Intrinsic Magnetic Properties in Permanent Magnets

Advanced Functional Materials, EarlyView.
Permanent magnets derive their extraordinary strength from deep, universal electronic‐structure principles that control magnetization, anisotropy, and intrinsic performance. This work uncovers those governing rules, examines modern modeling and AI‐driven discovery methods, identifies critical bottlenecks, and reveals electronic fingerprints shared ...
Prashant Singh
wiley   +1 more source

Dynamic optimization of stand structure in Pinus yunnanensis secondary forests based on deep reinforcement learning and structural prediction

Frontiers in Plant Science
IntroductionThe rational structure of forest stands plays a crucial role in maintaining ecosystem functions, enhancing community stability, and ensuring sustainable management.
Jian Zhao, Jianming Wang, Jiting Yin, Yuling Chen, Baoguo Wu   +4 more
doaj   +1 more source

Conductance‐Dependent Photoresponse in a Dynamic SrTiO3 Memristor for Biorealistic Computing

Advanced Functional Materials, EarlyView.
A nanoscale SrTiO3 memristor is shown to exhibit dynamic synaptic behavior through the interaction of local electrical and global optical signals. Its photoresponse depends quantitatively on the conductance state, which evolves and decays over tunable timescales, enabling ultralow‐power, biorealistic learning mechanisms for advanced in‐memory and ...
Christoph Weilenmann, Hanglin He, Marko Mladenović, Till Zellweger, Kevin Portner, Klemens Bauer, Guillaume Bellec, Mathieu Luisier, Alexandros Emboras   +8 more
wiley   +1 more source

From Clinic to Computation: Multiscale Bioengineering Strategies for Durable Biological Aortic Valve Replacements

Advanced Functional Materials, EarlyView.
Bioprosthetic aortic valves have revolutionized the treatment of aortic stenosis, but their durability is limited by structural valve deterioration (SVD). This review focuses on the pericardial tissue at the heart of these valves, examining how its mechanical properties and calcification drive fatigue and failure.
Gabriele Greco, Pierfrancesco Gaziano, Serena Anglese, Simone Morganti, Carlo de Vincentiis, Ferdinando Auricchio, Michele Marino, Michele Conti   +7 more
wiley   +1 more source

Practical Deep Reinforcement Learning Approach for Stock Trading

, 2018
Stock trading strategy plays a crucial role in investment companies. However, it is challenging to obtain optimal strategy in the complex and dynamic stock market. We explore the potential of deep reinforcement learning to optimize stock trading strategy
Liu, Xiao-Yang, Walid, Anwar, Xiong, Zhuoran, Yang, Hongyang, Zhong, Shan   +4 more
core  

Artificial Intelligence as the Next Visionary in Liquid Crystal Research

Advanced Functional Materials, EarlyView.
The functions of AI in the research laboratory are becoming increasingly sophisticated, allowing the entire process of hypothesis formulation, material design, synthesis, experimental design, and reiterative testing to be automated. In our work, we conceive how the incorporation of AI in the laboratory environment will transform the role and ...
Mert O. Astam, Ouassim L'Karkouri, Danqing Liu   +2 more
wiley   +1 more source

Z-Score Experience Replay in Off-Policy Deep Reinforcement Learning

Sensors
Reinforcement learning, as a machine learning method that does not require pre-training data, seeks the optimal policy through the continuous interaction between an agent and its environment.
Yana Yang, Meng Xi, Huiao Dai, Jiabao Wen, Jiachen Yang   +4 more
doaj   +1 more source

In Situ 3D Bioprinting: Impact of Cross‐Linking on the Adhesive Properties of Hydrogels

Advanced Functional Materials, EarlyView.
In situ 3D bioprinting enables the direct deposition of cell‐laden, adhesive biomaterials for on‐site tissue regeneration. This review provides a comprehensive overview of how cross‐linking influences the bioadhesive properties of hydrogels used in 3D bioprinting, highlighting cross‐linking triggers, bioadhesion mechanisms, polymer interpenetration ...
Odile Romero Fernandez, Mulazim Hussain Asim, Shumaila Arshad, Gergely Kali, Andreas Bernkop‐Schnürch   +4 more
wiley   +1 more source

Deep deterministic portfolio optimization

Journal of Finance and Data Science, 2020
Can deep reinforcement learning algorithms be exploited as solvers for optimal trading strategies? The aim of this work is to test reinforcement learning algorithms on conceptually simple, but mathematically non-trivial, trading environments.
Ayman Chaouki, Stephen Hardiman, Christian Schmidt, Emmanuel Sérié, Joachim de Lataillade   +4 more
doaj   +1 more source

Towards Deep Symbolic Reinforcement Learning

, 2016
Deep reinforcement learning (DRL) brings the power of deep neural networks to bear on the generic task of trial-and-error learning, and its effectiveness has been convincingly demonstrated on tasks such as Atari video games and the game of Go. However, contemporary DRL systems inherit a number of shortcomings from the current generation of deep ...
Garnelo, M, Arulkumaran, K, Shanahan, M
openaire   +3 more sources