Results 111 to 120 of about 96,348 (272)

Lattice Genome Framework for Regionally Tailored Component‐Level Multi‐Objective Design in Additive Manufacturing

Advanced Science, EarlyView.
A Lattice Genome framework links geometric and process “genes” to lattice “phenotypes” via correction‐calibrated high‐throughput simulations and a growing performance database. Genome‐driven retrieval and recombination of unit cells enables component‐level, regionally tailored multi‐objective design: stress fields are programmed under constant relative
Haoyuan Deng, Yufan Zhao, Mingyang Cao, Haiou Yang, Pan Wang, Hongze Wang, Hao Wang, Akihiko Chiba, Xin Lin   +8 more
wiley   +1 more source

Online Hyperparameter Tuning in Bayesian Optimization for Material Parameter Identification: An Application in Strain-Hardening Plasticity for Automotive Structural Steel

AppliedMath
Effective identification of strain-hardening parameters is essential for predictive plasticity models used in automotive applications. However, the performance of Bayesian optimization depends strongly on kernel hyperparameters in the Gaussian-process ...
Teng Long, Leyu Wang, Cing-Dao Kan, James D. Lee   +3 more
doaj   +1 more source

Mechanistic Analysis of Large Atomic Models of Molten Salt

Advanced Science, EarlyView.
This work uncovers the physical mechanism of large atomic models for molten salts by linking atomic contribution to electronic structure features. We demonstrate that energy predictions are physically determined by the local occupancy of frontier orbitals.
Yuliang Guo, Xiaobo Sun, Xiaoli Xi, Zuoren Nie   +3 more
wiley   +1 more source

Optimizing a Hybrid Deep Learning Model for DDoS Detection Using DBSCAN and PSO

Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)
This study proposes a hybrid deep learning approach that combines Gated Recurrent Units (GRUs) and Convolutional Neural Networks (CNNs) for Distributed Denial of Service (DDoS) cyberattack detection.
Indrastanti Ratna Widiasari, Rissal Efendi   +1 more
doaj   +1 more source

Understanding Fabrication Variability in Core‐Shell Soft Biomaterials Using Stochastic Artificial Intelligence

Advanced Science, EarlyView.
Fabrication‐induced variability remains a fundamental limitation in the scalable design of soft biomaterials. In this work, a stochastic machine learning approach based on Gaussian processes modeling is employed to establish quantitative links between biofabrication parameters, material properties, and their intrinsic variability.
Maria Alexaki, Lília M. S. Dias, Raquel C. Gonçalves, Dinis O. Abranches, Albano N. Carneiro Neto, Rute A. S. Ferreira, Paulo S. B. André, João F. Mano, Mariana B. Oliveira   +8 more
wiley   +1 more source

The Use of Hyperparameter Tuning in Model Classification: A Scientific Work Area Identification

JOIV: International Journal on Informatics Visualization
This research aims to investigate the effectiveness of hyperparameter tuning, particularly using Optuna, in enhancing the classification performance of machine learning models on scientific work reviews. The study focuses on automating the classification
Nadya Alinda Rahmi, Sarjon Defit, - Okfalisa   +2 more
doaj   +1 more source

Physics‐Embedded Neural Network: A Novel Approach to Design Polymeric Materials

Advanced Science, EarlyView.
Traditional black‐box models for polymer mechanics rely solely on data and lack physical interpretability. This work presents a physics‐embedded neural network (PENN) that integrates constitutive equations into machine learning. The approach ensures reliable stress predictions, provides interpretable parameters, and enables performance‐driven, inverse ...
Siqi Zhan, Hengheng Zhao, Haotian Wang, Zhanjie Liu, Qian Li, Weifeng Zhang, Qingsong Zhao, Liqun Zhang, Jun Liu   +8 more
wiley   +1 more source

Hyperparameter Optimization of Ensemble Learning for Heart Disease Prediction using Patient Data

Sistemasi: Jurnal Sistem Informasi
This study evaluates the impact of hyperparameter optimization on the performance of four machine learning algorithms—Extra Trees, XGBoost, Random Forest, and AdaBoost—in heart disease prediction. The results show that hyperparameter tuning significantly
Nikko Listio Wicaksono, Kusrini Kusrini
doaj   +1 more source

Forecasting Root Rot Disease through Predictive Microbial Functional Profiling

Advanced Science, EarlyView.
Predicting soil‐borne disease moves beyond observation with a framework that elevates microbial functional genes into reliable forecasting biomarkers. By coupling targeted qPCR assays for core stress‐response genes with machine learning, this method detects root rot risks in pre‐symptomatic soils with over 80% accuracy.
Chuan You, Peng Ren, Ying Guan, Kai Gong, Zhen Hua, Wuxian Zhou, Xinlan Mei, Yikui Wang, Xiaofang Wang, Yangchun Xu, Qirong Shen, Zhong Wei   +11 more
wiley   +1 more source

Hyperparameter Tuning Through Pessimistic Bilevel Optimization

Automated hyperparameter search in machine learning, especially for deep learning models, is typically formulated as a bilevel optimization problem, with hyperparameter values determined by the upper level and the model learning achieved by the lower-level problem.
Ustun, Meltem Apaydin, Xu, Liang, Zeng, Bo, Qian, Xiaoning   +3 more
openaire   +2 more sources