Results 131 to 140 of about 840,116 (317)
Flexible Memory: Progress, Challenges, and Opportunities
Flexible memory technology is crucial for flexible electronics integration. This review covers its historical evolution, evaluates rigid systems, proposes a flexible memory framework based on multiple mechanisms, stresses material design's role, presents a coupling model for performance optimization, and points out future directions.
Ruizhi Yuan+5 more
wiley +1 more source
This perspective highlights how knowledge‐guided artificial intelligence can address key challenges in manufacturing inverse design, including high‐dimensional search spaces, limited data, and process constraints. It focused on three complementary pillars—expert‐guided problem definition, physics‐informed machine learning, and large language model ...
Hugon Lee+3 more
wiley +1 more source
Joint Situational Assessment‐Hierarchical Decision‐Making Framework for Maneuver Intent Decisions
This study introduces a new framework for decision‐making in unmanned combat aerial vehicles (UCAVs), integrating graph convolutional networks and hierarchical reinforcement learning (HRL). The method tackles adopts a curriculum‐based training approach guided by cross‐entropy rewards.
Ruihai Chen+4 more
wiley +1 more source
A Fixed-Point Theorem for Commuting Monotone Functions [PDF]
William J. Gray
openalex +1 more source
This review aims to provide a broad understanding for interdisciplinary researchers in engineering and clinical applications. It addresses the development and control of magnetic actuation systems (MASs) in clinical surgeries and their revolutionary effects in multiple clinical applications.
Yingxin Huo+3 more
wiley +1 more source
A Generalization of Kannan's Fixed Point Theorem
In order to observe the condition of Kannan mappings, we prove a generalization of Kannan's fixed point theorem. Our theorem involves constants and we obtain the best constants to ensure a fixed point.
Enjouji Yusuke+2 more
doaj
Proof of the Fixed Point Theorems of Poincaré and Birkhoff [PDF]
Richard B. Barrar
openalex +1 more source
Predicting Performance of Hall Effect Ion Source Using Machine Learning
This study introduces HallNN, a machine learning tool for predicting Hall effect ion source performance using a neural network ensemble trained on data generated from numerical simulations. HallNN provides faster and more accurate predictions than numerical methods and traditional scaling laws, making it valuable for designing and optimizing Hall ...
Jaehong Park+8 more
wiley +1 more source