Results 121 to 130 of about 1,052,376 (334)

A Lightweight Procedural Layer for Hybrid Experimental–Computational Workflows in Materials Science

Advanced Engineering Materials, EarlyView.
We unveil a prototype hybrid‐workflow framework that fuses automatedcomputation with hands‐on experiments. Built atop pyiron, a lightweight, parameterized layer translates procedure descriptions into executable manual steps, syncing instrument settings, human interventions, and data capture in real‐time today.
Steffen Brinckmann, Sarath Menon, Raphael Röske, Johannes Steinhülb, Michael Selzer, Jan Janssen, Tilmann Hickel, Ruth Schwaiger, Jörg Neugebauer   +8 more
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

Deep reinforcement learning: Bridging learning and control in intelligent systems

Deep Reinforcement Learning (DRL) has gained popularity as a new approach in artificial intelligence that successfully integrates representation learning through Deep Learning with decision making in Reinforcement Learning.
Şahin, C. B., Raza, A., Zitar, R. A., Abualigah, L., Thanh, H. V., Smerat, A., Al-Okbi, N. K., Liu, Z., Izci, D.   +8 more
core   +1 more source

Influence of Geometric Design on Mechanical Performance of Auxetic Metastructure

Advanced Engineering Materials, EarlyView.
Strategic geometric reinforcement transforms auxetic performance. This study evaluates 3D‐printed arrowhead metastructures, revealing that a modified design with local ring reinforcement suppresses premature failure to achieve superior energy absorption and structural efficiency.
Muhammad Gulzari, Supharoek Sornsomrit, Muhammad Ayaz Akbar, Stephan Rudykh   +3 more
wiley   +1 more source

Deep Reinforcement Learning algorithms learn important classes of repeated games optimally—Theoretical and empirical analysis

Franklin Open
This paper evaluates two prominent Deep Reinforcement Learning algorithms, Deep Q-Learning and Twin Delayed Deep Deterministic Policy Gradient, by comparing their learned policies against analytically derived optimal policies in specific game-theoretic ...
Marvin Bongiovi
doaj   +1 more source

A Dislocation Perspective on Strength and Toughness in Ceramics

Advanced Engineering Materials, EarlyView.
Dislocations in ceramics enjoy a long but yet under‐appreciated history. The three research waves for dislocations in ceramics highlight the topic evolution over the last 90 years. This review focuses on the impact of dislocation on strength and toughness in ceramics.
Xufei Fang
wiley   +1 more source

Optimal Treatment Strategies for Critical Patients with Deep Reinforcement Learning

Personalized clinical decision support systems are increasingly being adopted due to the emergence of data-driven technologies, with this approach now gaining recognition in critical care.
Li, Lin, Cai, Taotao, Li, Qing, Tao, Xiaohui, Yong, Jianming, Xie, Haoran, Job, Simi   +6 more
core   +1 more source

Hierarchical deep reinforcement learning: Integrating temporal abstraction and intrinsic motivation [PDF]

, 2017
Learning goal-directed behavior in environments with sparse feedback is a major challenge for reinforcement learning algorithms. One of the key difficulties is insufficient exploration, resulting in an agent being unable to learn robust policies ...
Narasimhan, Karthik Rajagopal, Saeedi, Ardavan, Tenenbaum, Joshua B, Kulkarni, Tejas D.   +3 more
core  

Learning to Walk Via Deep Reinforcement Learning

Robotics: Science and Systems XV, 2019
RSS 2019, https://sites.google.com/view/minitaur-locomotion/
Tuomas Haarnoja, Aurick Zhou, Sehoon Ha, Jie Tan 0001, George Tucker, Sergey Levine   +5 more
openaire   +2 more sources

Machine Learning‐Supported Analysis for Predicting and Visualizing Nonlinear Relationships Between Material Properties in Electroplated Chromium Layers

Advanced Engineering Materials, EarlyView.
This study applies machine learning regression to predict chromium layer thickness in decorative trivalent chromium electroplating, using 441 experiments from laboratory‐scale (1L) and pilot‐scale (14L) setups. Tree‐based models, particularly CatBoost, outperformed linear regression by capturing nonlinear parameter interactions (R2$R^2$ up to 0.77 ...
Christoph Baumer, Christopher Mai, Luca Eisentraut, Andreas Bund, Ricardo Buettner   +4 more
wiley   +1 more source