Results 111 to 120 of about 22,826 (253)

Advanced Manufacturing of Composite‐Based Systems for Energy Applications

Advanced Materials Technologies, EarlyView.
Advanced manufacturing enables the integration of polymers, ceramics, metal oxides, and composites into architected microstructures with tailored transport pathways. By coupling material selection, manufacturing strategy, and structural design, multifunctional energy systems can simultaneously improve electrochemical performance, thermal management ...
Sri Vaishnavi Thummalapalli, Dhanush Patil, Libin Yang, M. Taylor Sobczak, Anshul Kulkarni, Xiao Sun, Arunachalam Ramanathan, Tiantian Li, Ramaraja P. Ramasamy, Jason J. Locklin, Yanyu Chen, Xianqiao Wang, Arunachala Mada Kannan, Kenan Song   +13 more
wiley   +1 more source

Stochastic gradient descent optimisation for convolutional neural network for medical image segmentation. [PDF]

Open Life Sci, 2023
Nagendram S, Singh A, Harish Babu G, Joshi R, Pande SD, Ahammad SKH, Dhabliya D, Bisht A.   +7 more
europepmc   +1 more source

Spheroid‐On‐A‐Drop: A Modular Droplet Microfluidics Platform

Advanced Materials Technologies, EarlyView.
The proposed Spheroid‐on‐a‐Drop platform represents a reproducible and tunable platform for the fabrication of biocompatible GelMA‐based microgels, able to support controlled 3D tumor spheroid culture. Its precise control over droplet dynamics and cell distribution paves the way for advanced applications in tissue modeling, drug screening, and ...
A. Fergola, V. Vighetto, G. Mesiano, A. Cosola, S. L. Marasso, M. Cocuzza, C. F. Pirri, L. Napione, F. Frascella   +8 more
wiley   +1 more source

Random coordinate descent: A simple alternative for optimizing parameterized quantum circuits

Physical Review Research
Variational quantum algorithms rely on the optimization of parameterized quantum circuits in noisy settings. The commonly used back-propagation procedure in classical machine learning is not directly applicable in this setting due to the collapse of ...
Zhiyan Ding, Taehee Ko, Jiahao Yao, Lin Lin, Xiantao Li   +4 more
doaj   +1 more source

Performance Analysis of an Optical System for FSO Communications Utilizing Combined Stochastic Gradient Descent Optimization Algorithm

Applied System Innovation
Wavefront aberrations caused by thermal flows or arising from the quality of optical components can significantly impair wireless communication links. Such aberrations may result in an increased error rate in the received signal, leading to data loss in ...
Ilya Galaktionov, Vladimir Toporovsky
doaj   +1 more source

A SIEVE STOCHASTIC GRADIENT DESCENT ESTIMATOR FOR ONLINE NONPARAMETRIC REGRESSION IN SOBOLEV ELLIPSOIDS. [PDF]

Ann Stat, 2022
Zhang T, Simon N.
europepmc   +1 more source

Stochastic Gradient Descent with Adaptive Data

Operations Research
Stochastic Gradient Descent with Adaptive Data Stochastic gradient descent (SGD) is a central tool in modern optimization, but its classical theory relies on the assumption that data are independent of the decisions being optimized. In many operations research settings, this assumption fails: policies influence system dynamics, and ...
Ethan Che, Jing Dong, Xin T. Tong
openaire   +3 more sources

Directional Latent Hybridization: Beyond Random Noise in Physics‐Informed Generative Inverse Design of Nonlinear Metamaterials

Advanced Materials Technologies, EarlyView.
A physics‐informed generative framework introduces Directional Latent Hybridization (DLH) for the deterministic inverse design of nonlinear metamaterials. By hybridizing dominant traits from parent geometries in the latent space, DLH overcomes the instabilities of stochastic models to ensure high structural precision at high densities.
Semin Ahn, Jun Young Choi, Sung‐Hoon Ahn   +2 more
wiley   +1 more source

Low‐Pressure Plasma‐Based Wrinkling of PDMS and Machine Learning‐Driven Property Engineering

Advanced Materials Technologies, EarlyView.
Wrinkled surfaces are well‐suited for controlled surface deformations in the µm range. The key challenge is the relation between the resulting wrinkle features and the necessary process conditions. Machine learning techniques have solved the prediction and inverse design problems for various preparation conditions, opening a precisely controlled ...
Fabian Kopsch, Atmika Bhardwaj, Mirko Nitschke, Ilka Hermes, Andreas Fery, Anett Müller, Marco Werner, André Knapp   +7 more
wiley   +1 more source

Multi-Depth Computer-Generated Hologram Based on Stochastic Gradient Descent Algorithm with Weighted Complex Loss Function and Masked Diffraction. [PDF]

Micromachines (Basel), 2023
Quan J, Yan B, Sang X, Zhong C, Li H, Qin X, Xiao R, Sun Z, Dong Y, Zhang H.   +9 more
europepmc   +1 more source