Results 81 to 90 of about 5,619,500 (318)

Bridging Nature and Technology: A Perspective on Role of Machine Learning in Bioinspired Ceramics

Advanced Engineering Materials, EarlyView.
Machine learning (ML) is revolutionizing the development of bioinspired ceramics. This article investigates how ML can be used to design new ceramic materials with exceptional performance, inspired by the structures found in nature. The research highlights how ML can predict material properties, optimize designs, and create advanced models to unlock a ...
Hamidreza Yazdani Sarvestani, Aditi Singh, Behnam Ashrafi   +2 more
wiley   +1 more source

Static and Dynamic Behavior of Novel Y‐Shaped Sandwich Beams Subjected to Compressive Loadings: Integration of Supervised Learning and Experimentation

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, Saeed Kamarian, Mahdi Bodaghi, Saeed Saber‐Samandari, Jung‐il Song   +4 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

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, Surabhi Nadigotti, Erfan Fatehi, Derek Aranguren van Egmond, Behnam Ashrafi   +4 more
wiley   +1 more source

Multiagent Deep Reinforcement Learning Algorithms in StarCraft II: A Review

IEEE Access
StarCraft 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

Advancing Wildfire‐Retardant Materials: Engineering Strategies for Direct and Indirect Suppression

Advanced Engineering Materials, EarlyView.
Here, the evolution, ecological impact, and performance of current fire‐retardant materials and suppression strategies are reviewed, offering an engineering perspective to address existing challenges and propose pathways for the development of more effective, scalable, and sustainable solutions to meet the demands of a changing climate. Wildfires cause
Changxin Dong, Samya Sen, Athena Kolli, Stewart G. Wilson, Eric Andrew Appel   +4 more
wiley   +1 more source

Improving sample efficiency and exploration in upside-down reinforcement learning

Journal of Information and Intelligence
Supervised learning has been demonstrated to be a stable approach for training deep neural networks. Upside-down reinforcement learning solves reinforcement learning problems by using supervised learning, but this method suffers from weak sample ...
Mohammadreza Nakhaei, Reza Askari Moghadam   +1 more
doaj   +1 more source

Using Inverse Reinforcement Learning with Real Trajectories to Get More Trustworthy Pedestrian Simulations

Mathematics, 2020
Reinforcement learning is one of the most promising machine learning techniques to get intelligent behaviors for embodied agents in simulations. The output of the classic Temporal Difference family of Reinforcement Learning algorithms adopts the form of ...
Francisco Martinez-Gil, Miguel Lozano, Ignacio García-Fernández, Pau Romero, Dolors Serra, Rafael Sebastián   +5 more
doaj   +1 more source

New Developments in the Field of Production and Application of Multi‐Material Wire Arc Additive Manufacturing Components: A Review

Advanced Engineering Materials, EarlyView.
The utilization of direct energy deposition (DED)‐arc additive manufacturing processes in industrial applications is increasing, and these processes have the potential for multi‐material applications. This work provides a overview of the state of research in DED‐arc made functional graded structures, to establish a link to potential industrial ...
Kai Treutler, Volker Wesling
wiley   +1 more source

Constructive reinforcement learning [PDF]

International Journal of Intelligent Systems, 2000
Summary: This paper presents an operative measure of reinforcement for constructive learning methods, i.e., eager learning methods using highly expressible (or universal) representation languages. These evaluation tools allow a further insight in the study of the growth of knowledge, theory revision, and abduction.
openaire   +3 more sources