Results 161 to 170 of about 6,929,542 (320)

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

A New Hyperparameter Tuning Framework for Regression Tasks in Deep Neural Network: Combined-Sampling Algorithm to Search the Optimized Hyperparameters

Mathematics
This paper introduces a novel hyperparameter optimization framework for regression tasks called the Combined-Sampling Algorithm to Search the Optimized Hyperparameters (CASOH).
Nguyen Huu Tiep, Hae-Yong Jeong, Kyung-Doo Kim, Nguyen Xuan Mung, Nhu-Ngoc Dao, Hoai-Nam Tran, Van-Khanh Hoang, Nguyen Ngoc Anh, Mai The Vu   +8 more
doaj   +1 more source

ML Workflows for Screening Degradation‐Relevant Properties of Forever Chemicals

Advanced Science, EarlyView.
The environmental persistence of per‐ and polyfluoroalkyl substances (PFAS) necessitates efficient remediation strategies. This study presents physics‐informed machine learning workflows that accurately predict critical degradation properties, including bond dissociation energies and polarizability.
Pranoy Ray, Andrew R. Castillo, Manoj Kolel‐Veetil, Surya R. Kalidindi   +3 more
wiley   +1 more source

Strategies of Automated Machine Learning for Energy Sustainability in Green Artificial Intelligence

Applied Sciences
Automated machine learning (AutoML) is recognized for its efficiency in facilitating model development due to its ability to perform tasks autonomously, without constant human intervention.
Dagoberto Castellanos-Nieves, Luis García-Forte   +1 more
doaj   +1 more source

Dynamic Priors in Bayesian Optimization for Hyperparameter Optimization

CoRR
Bayesian optimization (BO) is a widely used approach to hyperparameter optimization (HPO). However, most existing HPO methods only incorporate expert knowledge during initialization, limiting practitioners' ability to influence the optimization process as new insights emerge. This limits the applicability of BO in iterative machine learning development
Lukas Fehring, Marcel Wever, Maximilian Spliethöver, Leona Hennig, Henning Wachsmuth, Marius Lindauer   +5 more
openaire   +2 more sources

Performance–Complexity Trade‐Offs in Battery Lifetime Prediction with Task‐Aware Transformers

Advanced Science, EarlyView.
FAST‐BatPro integrates convolutional feature extraction, flash Attention, and sparse attention for efficient battery lifetime prediction. Using limited early‐cycle data across multiple chemistries and operating conditions, it achieves robust accuracy while reducing inference latency, computational cost, and energy consumption.
Jingyuan Zhao, Misheng Cai, Zhenghong Wang, Yan Wang, Yuqi Li, Bo Dong, Hewu Wang, Andrew F. Burke, Simona Onori, Stephen J. Harris   +9 more
wiley   +1 more source

Ensemble effort estimation with metaheuristic hyperparameters and weight optimization for achieving accuracy.

PLoS ONE
Software development effort estimation (SDEE) is recognized as vital activity for effective project management since under or over estimating can lead to unsuccessful utilization of project resources.
Anum Yasmin, Wasi Haider Butt, Ali Daud
doaj   +1 more source

Metaheuristic-Based Hyperparameter Optimization for Machine Learning Classification: An Applied Experimental Study

Iraqi Journal for Computers and Informatics
The selection of hyperparameters is a key factor in the predictive performance and the overall generalization of machine learning models. In real-life scenarios, poor hyperparameter selection tends to result in suboptimal performance, despite the use of ...
ahmed majid
doaj   +1 more source

Overcoming Artificial Structures in Resolution‐Enhanced Hi‐C Data by Signal Decomposition and Multi‐Scale Attention

Advanced Science, EarlyView.
Deep‐learning‐based signal enhancement is an effective way to recover high‐resolution details from a low‐resolution chromatin contact map. However, due to computational challenges, existing methods commonly divide up the contact map into small patches and create artificial discontinuities at patch boundaries.
Qinyao Li, Kelly Yichen Li, Chiara Nicoletti, Stephen Kwok‐Wing Tsui, Pier Lorenzo Puri, Qin Cao, Kevin Y. Yip   +6 more
wiley   +1 more source

High‐Throughput Data Generation and Transfer Learning Enabled Microstructure‐Property Integrated Design of Nickel‐Based Powder Metallurgy Superalloy

Advanced Science, EarlyView.
An integrated transfer learning framework integrates CALPHAD simulations, diffusion‐multiple experiments, and literature data to predict long‐term microstructural stability and short‐term mechanical properties of Ni‐based powder metallurgy superalloys. Based on these model predictions, a high‐performance, low‐density alloy, USTB‐PM750, is designed from
Zixin Li, Hongtao Zhang, Zichao Peng, Yuheng Zhang, Qian Lv, Yihuan Cao, Xuqing Wang, Huadong Fu, Jianxin Xie   +8 more
wiley   +1 more source