Results 51 to 60 of about 434,301 (280)

A Workflow to Accelerate Microstructure‐Sensitive Fatigue Life Predictions

Advanced Engineering Materials, EarlyView.
This study introduces a workflow to accelerate predictions of microstructure‐sensitive fatigue life. Results from frameworks with varying levels of simplification are benchmarked against published reference results. The analysis reveals a trade‐off between accuracy and model complexity, offering researchers a practical guide for selecting the optimal ...
Luca Loiodice, Krzysztof S. Stopka, Michael D. Sangid   +2 more
wiley   +1 more source

Bayesian optimized CNN ensemble for efficient potato blight detection using fuzzy image enhancement

Scientific Reports
Potato blight is a serious disease that affects potato crops and leads to substantial agricultural and economic losses. To enhance detection accuracy, we propose Bayesian Optimized CNN Weighted Ensemble Potato Blight Detection, a deep learning-based ...
Achin Jain, Arun Kumar Dubey, Vincent Shin-Hung Pan, Saoucene Mahfoudh, Turki A. Althaqafi, Varsha Arya, Wadee Alhalabi, Sunil K. Singh, Vanita Jain, Arvind Panwar, Sudhakar Kumar, Ching-Hsien Hsu, Brij B. Gupta   +12 more
doaj   +1 more source

Dynamic Bayesian Network Modeling Based on Structure Prediction for Gene Regulatory Network

IEEE Access, 2021
Gene regulatory network can intuitively reflect the interaction between genes, and an in-depth study of these relationships plays a significant role in the treatment and prevention of clinical diseases.
Luxuan Qu, Zhiqiong Wang, Chan Li, Shanghui Guo, Junchang Xin, Yuezhou Zhou, Weiyiqi Wang   +6 more
doaj   +1 more source

Additive Gaussian Process Regression for Predictive Design of High‐Performance, Printable Silicones

Advanced Engineering Materials, EarlyView.
A chemistry‐aware design framework for tuning printable polydimethylsiloxane (PDMS) for vat photopolymerization (VPP) is developed using additive Gaussian process (GP) modeling. Polymer network mechanics informs variable groupings, feasible formulation constraints, and interaction variables.
Roxana Carbonell, Martha Leach, Hongtao Song, Carolyn Seepersad   +3 more
wiley   +1 more source

Multimodal Data‐Driven Microstructure Characterization

Advanced Engineering Materials, EarlyView.
A self‐consistent autonomous workflow for EBSP‐based microstructure segmentation by integrating PCA, GMM clustering, and cNMF with information‐theoretic parameter selection, requiring no user input. An optimal ROI size related to characteristic grain size is identified.
Qi Zhang, Santiago Benito, Özge Özgün, Sebastian Weber, Markus Stricker   +4 more
wiley   +1 more source

High-Order Stochastic Gradient Thermostats for Bayesian Learning of Deep Models

, 2015
Learning in deep models using Bayesian methods has generated significant attention recently. This is largely because of the feasibility of modern Bayesian methods to yield scalable learning and inference, while maintaining a measure of uncertainty in the
Carin, Lawrence, Chen, Changyou, Fan, Kai, Li, Chunyuan   +3 more
core   +1 more source

Bayesian machine learning for financial modeling [PDF]

, 2021
Machine Learning (ML) is so pervasive in our todays life that we don't even realise that, more often than expected, we are using systems based on it. It is also evolving faster than ever before. When deploying ML systems that make decisions on their own, we need to think about their ignorance of our uncertain world.
openaire   +3 more sources

Local Bayesian Dirichlet mixing of imperfect models

Scientific Reports, 2023
To improve the predictability of complex computational models in the experimentally-unknown domains, we propose a Bayesian statistical machine learning framework utilizing the Dirichlet distribution that combines results of several imperfect models. This
Vojtech Kejzlar, Léo Neufcourt, Witold Nazarewicz   +2 more
doaj   +1 more source

Goal-Driven Dynamics Learning via Bayesian Optimization

, 2017
Real-world robots are becoming increasingly complex and commonly act in poorly understood environments where it is extremely challenging to model or learn their true dynamics. Therefore, it might be desirable to take a task-specific approach, wherein the
Bansal, Somil, Calandra, Roberto, Levine, Sergey, Tomlin, Claire J., Xiao, Ted   +4 more
core   +1 more source

An Experimental High‐Throughput Approach for the Screening of Hard Magnet Materials

Advanced Engineering Materials, EarlyView.
An entire workflow for the high‐throughput characterization and analysis of compositionally graded magnetic films is presented. Characterization protocols, data management tools and data analysis approaches are illustrated with test case Sm(Fe, V)12 based films.
William Rigaut, Pierre Le Berre, Hamdi Jaballah, Lukas Fink, Victor Poline, Richard Haettel, Stéphane Grenier, Gilbert Chahine, Ewen Bellec, Guillaume Buatier, Wilfried Hortschitz, Santa Pile, Thomas Schrefl, Nils Blanc, Yuan Hong, Thibaut Devillers, Nora M. Dempsey   +16 more
wiley   +1 more source