Results 81 to 90 of about 544,487 (316)
Advanced Engineering Materials, EarlyView.In this study, the mechanical response of Y‐shaped core sandwich beams under compressive loading is investigated, using deep feed‐forward neural networks (DFNNs) for predictive modeling. The DFNN model accurately captures stress–strain behavior, influenced by design parameters and loading rates.
Ali Khalvandi +4 more
wiley +1 more source
Multiagent Deep Reinforcement Learning Algorithms in StarCraft II: A Review
IEEE AccessStarCraft II, as a real-time strategy game, features multiagent collaboration, complex decision-making processes, partially observable environments, and long-term credit assignment; thus, it is an ideal platform for exploring, validating, and optimizing ...
Yanyan Li, Yijun Wang, Yiwei Zhou
doaj +1 more source
Advanced Engineering Materials, EarlyView.Herein, silicon‐based nanoparticle coatings on X2CrNiMo17‐12‐2 metal powder are presented. The coating process scale, process parameters, nanoparticle size (65–200 nm) as well as the coating amount are discussed regarding powder properties. The surface roughness affects the flowability, while reflectance depends on the coating material and surface ...
Arne Lüddecke +4 more
wiley +1 more source
A Survey on Offline Model-Based Reinforcement Learning [PDF]
arXiv, 2023 Model-based approaches are becoming increasingly popular in the field of offline reinforcement learning, with high potential in real-world applications due to the model's capability of thoroughly utilizing the large historical datasets available with supervised learning techniques. This paper presents a literature review of recent work in offline model-
arxiv
Beyond Order: Perspectives on Leveraging Machine Learning for Disordered Materials
Advanced Engineering Materials, EarlyView.This article explores how machine learning (ML) revolutionizes the study and design of disordered materials by uncovering hidden patterns, predicting properties, and optimizing multiscale structures. It highlights key advancements, including generative models, graph neural networks, and hybrid ML‐physics methods, addressing challenges like data ...
Hamidreza Yazdani Sarvestani +4 more
wiley +1 more source
Advanced Functional Materials, EarlyView.Ultrathin, flexible neural probes are developed with an innovative, biomimetic design incorporating brain tissue‐compatible materials. The material system employs biomolecule‐based encapsulation agents to mitigate inflammatory responses, as demonstrated through comprehensive in vitro and in vivo studies.
Jeonghwa Jeong +7 more
wiley +1 more source
Relational Reinforcement Learning
, 2001 This paper presents an introduction to reinforcement learning and relational reinforcement learning at a level to be understood by students and researchers with different backgrounds.It gives an overview of the fundamental principles and techniques of reinforcement learning without involving a rigorous deduction of the mathematics involved through the ...
openaire +3 more sources
Chiral Engineered Biomaterials: New Frontiers in Cellular Fate Regulation for Regenerative Medicine
Advanced Functional Materials, EarlyView.Chiral engineered biomaterials can selectively influence cell behaviors in regenerative medicine. This review covers chiral engineered biomaterials in terms of their fabrication methods, cellular response mechanisms, and applications in directing stem cell differentiation and tissue function.
Yuwen Wang +5 more
wiley +1 more source
Augmented Q Imitation Learning (AQIL) [PDF]
arXiv, 2020 The study of unsupervised learning can be generally divided into two categories: imitation learning and reinforcement learning. In imitation learning the machine learns by mimicking the behavior of an expert system whereas in reinforcement learning the machine learns via direct environment feedback.
arxiv