Results 161 to 170 of about 19,041 (309)

CONVERGENCE OF MARKOV CHAIN APPROXIMATIONS TO STOCHASTIC REACTION DIFFUSION EQUATIONS [PDF]

In the context of simulating the transport of a chemical or bacterial contaminant through a moving sheet of water, we extend a well established method of approximating reaction-diffusion equations with Markov chains by allowing convection, certain ...
Michael A. Kouritzin, Hongwei Long
core  

Fully Programmable Slow Light Based on a Spinor Representation of Generalized Coupled‐Resonator‐Induced Transparency

Advanced Science, EarlyView.
A programmable coupled‐resonator‐induced transparency (CRIT) platform is proposed, generalizing classical electromagnetically induced transparency (EIT) via a spinor representation. By utilizing dual‐channel gauge fields and universal unitary operations, this architecture enables dynamically engineered, reconfigurable slow‐light bands on a silicon ...
Seungkyun Park, Beomjoon Chae, Hyungchul Park, Sunkyu Yu, Xianji Piao, Namkyoo Park   +5 more
wiley   +1 more source

用随机无网格点插值法分析齿轮弯曲疲劳强度的可靠性

Jixie chuandong, 2005
The bending fatigue reliability of gear teeth has been analyzed by perturbation stochastic meshless point interpolation method（PSMPIM）.In the meshless point interpolation method the problem domain is represented by properly scattered points.A technique ...
游世辉, 冯云华, 钟志华, 李光耀   +3 more
doaj  

Fundamental Challenges, Physical Implementations, and Integration Strategies for Ising Machines in Large‐Scale Optimization Tasks

Advanced Electronic Materials, EarlyView.
Ising machines are emerging as specialized hardware solvers for computationally hard optimization problems. This review examines five major platforms—digital CMOS, analog CMOS, emerging devices, coherent optics, and quantum systems—highlighting physics‐rooted advantages and shared bottlenecks in scalability and connectivity.
Hyunjun Lee, Joon Pyo Kim, Sanghyeon Kim
wiley   +1 more source

Hysteretic Behavior of Random Particulate Composites by the Stochastic Finite Element Method. [PDF]

Materials (Basel), 2019
Sokołowski D, Kamiński M.
europepmc   +1 more source

On the Role of Preprocessing and Memristor Dynamics in Reservoir Computing for Image Classification

Advanced Electronic Materials, EarlyView.
ABSTRACT Reservoir computing (RC) is an emerging recurrent neural network architecture that has attracted growing attention for its low training cost and modest hardware requirements. Memristor‐based circuits are particularly promising for RC, as their intrinsic dynamics can reduce network size and parameter overhead in tasks such as time‐series ...
Rishona Daniels, Duna Wattad, Ronny Ronen, David Saad, Shahar Kvatinsky   +4 more
wiley   +1 more source

Stochastic Finite Element Analysis Framework for Modelling Mechanical Properties of Particulate Modified Polymer Composites. [PDF]

Materials (Basel), 2019
Ahmadi Moghaddam H, Mertiny P.
europepmc   +1 more source

Assessing Mesoscale Heterogeneities in Hard Carbon Electrodes Through Deep Learning‐Assisted FIB‐SEM Characterization, Manufacturing and Electrochemical Modeling

Advanced Energy Materials, EarlyView.
A combination of discrete and finite element method models for the current collector deformation and electrochemical performance analysis, respectively. The models are calibrated and validated with electrochemical and imaging data of hard carbon electrodes. These electrodes were manufactured with different parameters (slurry solid contents of 35 and 40
Soorya Saravanan, Imelda Cardenas‐Sierra, Pei Sun, Hari Raj, Daniele Duranti, Mahnoor Nafees, Edoardo Rossi, Marco Sebastiani, Paul Vigneaux, Martin Petit, Maxime Moreaud, Francisco Fernandez, Alejandro A. Franco   +12 more
wiley   +1 more source

Buckling Analysis of Vacancy-Defected Graphene Sheets by the Stochastic Finite Element Method. [PDF]

Materials (Basel), 2018
Chu L, Shi J, Ben S.
europepmc   +1 more source

Machine Learning Interatomic Potentials for Energy Materials: Architectures, Training Strategies, and Applications

Advanced Energy Materials, EarlyView.
Machine learning interatomic potentials bridge quantum accuracy and computational efficiency for materials discovery. Architectures from Gaussian process regression to equivariant graph neural networks, training strategies including active learning and foundation models, and applications in solid‐state electrolytes, batteries, electrocatalysts ...
In Kee Park, Saeed Pourasad, Jinhong Mun, Arunkumar Chitteth Rajan, Tae Hyeon Park, Anand Rohit, Mohammad Zafari, Tan‐Lien Pham, Eunjae Jung, Bonseung Koo, Miran Ha, Gi Beom Sim, Hyun Gyu Park, Won Woong Choi, Kwanghyeon Jo, Soohaeng Yoo Willow, David ChangMo Yang, Chang Woo Myung, Geunsik Lee, Kwang S. Kim   +19 more
wiley   +1 more source