Results 191 to 200 of about 5,465 (262)

AI‐BioMech: Deep Learning Prediction of Mechanical Behavior in Aperiodic Biological Cellular Materials

Advanced Intelligent Discovery, EarlyView.
AI‐BioMech is a deep learning framework that predicts the mechanical behavior of biological cellular materials directly from 2D images. By replacing traditional finite element analysis with semantic segmentation, it identifies stress and strain distributions with 99% accuracy, offering a high‐speed, scalable alternative for analyzing complex, aperiodic
Haleema Sadia, Marcelo A. Dias, Parvez Alam   +2 more
wiley   +1 more source

Tissue scaffold architecture affects implant degradation and bone tissue regeneration: A novel in silico mechanobiological model analysing cell behavior, mechanical stress and degradation kinematics. [PDF]

PLoS One
Alshammari A, Alabdah F, Li L, Cooper G.
europepmc   +1 more source

Robust Reinforcement Learning Control Framework for a Quadrotor Unmanned Aerial Vehicle Using Critic Neural Network

Advanced Intelligent Systems, Volume 7, Issue 3, March 2025.
Quadrotor unmanned aerial vehicle control is critical to maintain flight safety and efficiency, especially when facing external disturbances and model uncertainties. This article presents a robust reinforcement learning control scheme to deal with these challenges.
Yu Cai, Yefeng Yang, Tao Huang, Boyang Li   +3 more
wiley   +1 more source

Lidar‐Based Object Tracking of Traffic Participants with Sensor Nodes in Existing Urban Infrastructure

Advanced Intelligent Systems, EarlyView.
This paper presents a lidar‐based sensor node design and a rule‐based state observer for edge‐based traffic participant tracking. Unlike other state‐of‐the‐art methods, this state observer enables real‐time, CPU‐only edge processing without relying on machine learning approaches.
Simon Schäfer, Bassam Alrifaee, Ehsan Hashemi   +2 more
wiley   +1 more source

Multi-Scale Numerical Simulation of Fatigue Crack Propagation Mechanisms in the Heat-Affected Zone of AH36 Steel Welds. [PDF]

Materials (Basel)
Shen C, Fu Y, Zhao W, Yang J.
europepmc   +1 more source

Programmable Flocks: Hierarchical Swarm Control via Dynamic Parameter Injection

Advanced Intelligent Systems, EarlyView.
We present a method for the control of robot swarms which allows the shaping and the translation of patterns of simple robots (“smart particles”), using two types of devices. These two types represent a hierarchy: a larger group of simple, oblivious robots (which we call the workers) that is governed by simple local attraction forces and a smaller ...
Vivek Shankar Varadharajan, Giovanni Beltrame   +1 more
wiley   +1 more source

Underexplored Catalysts as General Structures: Application of Machine Learning Techniques for Reaction‐Specific Datasets

Angewandte Chemie, EarlyView.
Bias‐aware machine learning identified an underexplored imidazolidinone catalyst with broad competitive performance in iminium‐based reactions. Experimental benchmarking shows how data‐driven prioritization can uncover overlooked catalyst scaffolds from sparse and historically biased literature data.
Jiajing Li, Isaiah O. Betinol, Junshan Lai, Soresu Juyo, Jolene P. Reid   +4 more
wiley   +2 more sources

A robust strategy for FE model updating of composite panels using a combined ANN-SMA surrogate-assisted optimization framework. [PDF]

Sci Rep
Kouhi M, Mojtahedi A, Lotfollahi-Yaghin MA.   +2 more
europepmc   +1 more source

Identifying Dynamical Quantum Phase Transitions With a Migratable Quantum‐Classical Hybrid Neural Network

Advanced Intelligent Systems, EarlyView.
A hybrid quantum‐classical architecture is introduced to accurately identify dynamical quantum phase transitions from time‐evolved quantum states. The QCNN serves as a quantum dynamical feature extractor, while the classical network learns temporal correlations from a low‐dimensional readout sequence. The framework attains high accuracy, remains robust
Daili Li, Xin–Xin Yang, Xiaoting Wang, Wei Zhang   +3 more
wiley   +1 more source

In Situ Monitoring Network for Deposition Morphology and Residual Stress Reconstruction. [PDF]

Materials (Basel)
Lu Y, Huang H, Huang X, Wang C, Li W, Wu B.   +5 more
europepmc   +1 more source