Results 91 to 100 of about 56,129 (265)

Laser material processing optimization using bayesian optimization: a generic tool

Light: Advanced Manufacturing
Optimizing laser processes is historically challenging, requiring extensive and costly experimentation. To solve this issue, we apply Bayesian optimization for process parameter optimization to laser cutting, welding, and polishing.
Tobias Menold, Volkher Onuseit, Matthias Buser, Michael Haas, Nico Bär, Andreas Michalowski   +5 more
doaj   +1 more source

Weaving Intelligence: Thermally Drawn Multimaterial Fibers Toward AI‐Enabled Smart Textiles

Advanced Materials, EarlyView.
Thermally drawn multimaterial fibers are rapidly advancing as intelligent structural units for next‐generation smart textiles. Integrating multimaterial architectures with neuromorphic and spiking‐neural‐network principles enables fabrics that can sense, compute, and adapt autonomously.
Vuong Dinh Trung, Jiaxiang Yi, Hieu Nguyen‐Duc, Tien Nu Hoang Lo, Weili Zhao, Chaoqun Dong, Fabien Sorin, Baris Caglar, Lei Wei, Tung Nguyen‐Dang   +9 more
wiley   +1 more source

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

Machine Learning Accelerated Computational Design of Bio‐Inspired Catalysts in the Nitrogen Reduction Reaction

Advanced Materials, EarlyView.
We introduce a computational workflow that combines quantum chemical calculations and machine learning techniques to predict the catalytic performance of a wide range of catalysts in the nitrogen reduction reaction (NRR). The analysis of the trained models provides insights into the complex structure–activity relationship in experimental catalytic ...
Leonardo Di Ciano, Zihan You, Haoran Chen, Qifan Zhong, Rong‐Zhen Liao, Shaoqi Zhan   +5 more
wiley   +1 more source

A general Bayesian algorithm for the autonomous alignment of beamlines

Journal of Synchrotron Radiation
Autonomous methods to align beamlines can decrease the amount of time spent on diagnostics, and also uncover better global optima leading to better beam quality.
Thomas W. Morris, Max Rakitin, Yonghua Du, Mikhail Fedurin, Abigail C. Giles, Denis Leshchev, William H. Li, Brianna Romasky, Eli Stavitski, Andrew L. Walter, Paul Moeller, Boaz Nash, Antoine Islegen-Wojdyla   +12 more
doaj   +1 more source

Simulation based Bayesian Optimization

Statistics and Computing
Bayesian Optimization (BO) is a powerful method for optimizing black-box functions by combining prior knowledge with ongoing function evaluations. BO constructs a probabilistic surrogate model of the objective function given the covariates, which is in turn used to inform the selection of future evaluation points through an acquisition function.
Roi Naveiro, Becky Tang
openaire   +2 more sources

Bidirectional Process Prediction in the Laser‐Induced‐Graphene Production Using Blackbox Deep Learning

Advanced Materials Technologies, EarlyView.
This study shows that a lightweight blackbox neural network provides a practical, cost‐effective solution for bidirectional process prediction in laser‐induced graphene (LIG) fabrication. Achieving high predictive performance with minimal overhead, the approach democratizes machine learning (ML) for resource‐limited environments.
Maxim Polomoshnov, Nitheesh M. Nair, Gerardo Hernández‐Sosa, Markus Reischl   +3 more
wiley   +1 more source

Data‐Efficient Electromagnetic Surrogate Solver Through Dissipative Relaxation Transfer Learning

Advanced Optical Materials, EarlyView.
Dissipative relaxation transfer learning (DIRTL) enables data‐efficient training of electromagnetic surrogate solvers by pretraining data generated with artificial material loss before fine‐tuning on target lossless data. The framework suppresses resonant outlier effects during early training, allowing effective adaptation to high‐amplitude resonances ...
Sunghyun Nam, Chan Y. Park, Min Seok Jang   +2 more
wiley   +1 more source

Bayesian‐Optimized Dual‐Mode Luminescence‐Based Optical Thermometry Enabled by Negative Thermal Expansion in Lu2(MoO4)3:Yb–Er–Tm Phosphor

Advanced Optical Materials, EarlyView.
A Bayesian optimization framework identifies the ideal composition for Lu2(MoO4)3:Yb–Er–Tm phosphors with minimal experimental trials. By leveraging the host's negative thermal expansion, the material achieves remarkable thermal quenching compensation.
Reiko Furukawa, Takuya Hasegawa, Tong Fang, Saneyuki Ohno, Tomoyo Goto, Yasushi Sato, Ayahisa Okawa, Shu Yin   +7 more
wiley   +1 more source

Multi-Objective Batch Energy-Entropy Acquisition Function for Bayesian Optimization

Mathematics
Bayesian Optimization (BO) provides an efficient framework for optimizing expensive black-box functions by employing a surrogate model (typically a Gaussian Process) to approximate the objective function and an acquisition function to guide the search ...
Hangyu Zhu, Xilu Wang
doaj   +1 more source