Results 111 to 120 of about 236,656 (279)

Optimize Gate-All-Around Devices Using Wide Neural Network-Enhanced Bayesian Optimization

IEEE Journal of the Electron Devices Society
Device design processes based on manual design experience require numerous experiments and simulations. As transistors continue to shrink, complex physical effects, such as quantum effects intensify, making the design process increasingly costly, whether
Jiaye Shen, Zhiqiang Li, Zhenjie Yao
doaj   +1 more source

Inversion of the Impedance Response Towards Physical Parameter Extraction Using Interpretable Machine Learning

Advanced Energy Materials, EarlyView.
ABSTRACT Interpreting the impedance response of perovskite solar cells (PSCs) is challenging due to the complex coupling of ionic and electronic motion. While drift‐diffusion (DD) modelling is a reliable method, its mathematical complexity makes directly extracting physical parameters from experimental data infeasible.
Mahmoud Nabil, Isel Grau, Ricardo Grau‐Crespo, Said Hamad, Juan A. Anta   +4 more
wiley   +1 more source

A Design Methodology for Fault-Tolerant Neuromorphic Computing Using Bayesian Neural Network. [PDF]

Micromachines (Basel), 2023
Gao D, Xie X, Wei D.
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

Bayesian neural networks for macroeconomic analysis

Journal of Econometrics
JEL: C11, C30, C45, C53, E3, E44.
Hauzenberger, Niko, Huber, Florian, Klieber, Karin, Marcellino, Massimilano   +3 more
openaire   +4 more sources

Smart Exploration of Perovskite Photovoltaics: From AI Driven Discovery to Autonomous Laboratories

Advanced Energy Materials, EarlyView.
In this review, we summarize the fundamentals of AI in automated materials science, and review AI applications in perovskite solar cells. Then, we sum up recent progress in AI‐guided manufacturing optimization, and highlight AI‐driven high‐throughput and autonomous laboratories.
Wenning Chen, Zheyu Zhang, Kelvian T. Mularso, Bonghyun Jo, Hyun Suk Jung   +4 more
wiley   +1 more source

Robust Motor Imagery Tasks Classification Approach Using Bayesian Neural Network. [PDF]

Sensors (Basel), 2023
Milanés-Hermosilla D, Trujillo-Codorniú R, Lamar-Carbonell S, Sagaró-Zamora R, Tamayo-Pacheco JJ, Villarejo-Mayor JJ, Delisle-Rodriguez D.   +6 more
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

Structural Health Monitoring Impact Classification Method Based on Bayesian Neural Network. [PDF]

Polymers (Basel), 2022
Yu H, Seno AH, Sharif Khodaei Z, Aliabadi MHF.   +3 more
europepmc   +1 more source

Bayesian Sheaf Neural Networks

32 pages, 4 ...
Gillespie, Patrick, Hamdan, Layal Bou, Schizas, Ioannis, Boothe, David L., Maroulas, Vasileios   +4 more
openaire   +2 more sources