Results 151 to 160 of about 157,395 (292)

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

Cosmological Bayesian Model Selection: Recent Advances and Open Challenges

, 2012
The cosmology community has been increasingly focusing on Bayesian model selection as a tool to discriminate between competing theories to explain a large amount of data about our Universe.
Trotta R., Roberto Trotta
core   +1 more source

MFPD: A Multiple Fungal Pathogen Detection Pipeline Across Diverse Habitats

Advanced Science, EarlyView.
The MFPD pipeline integrates a comprehensive ITS reference database of fungal pathogens, optimized parameters, and algorithms tailored for both full‐length and subregion sequences that balance accuracy and computational efficiency; it enables high‐throughput, species‐level identification from amplicon sequencing data, supporting large‐scale ...
Yi Shen, Xinrun Yang, Jiabao Yu, Yaozhong Zhang, Tianjie Yang, Yang Gao, Xiaofang Wang, Alexandre Jousset, Waseem Raza, Fang‐Jie Zhao, Qirong Shen, Gaofei Jiang, Zhong Wei, Yangchun Xu   +13 more
wiley   +1 more source

Bayesian Model Selection in the Analysis of Cointegration [PDF]

In this paper we present the Bayesian model selection procedure within the class of cointegrated processes. In order to make inference about the cointegration space we use the class of Matrix Angular Central Gaussian distributions. To carry out posterior
Justyna Wróblewska
core  

Bayesian model selection for variable-coefficient partial differential equation discovery

Results in Engineering
Data-driven discovery of partial differential equations (PDEs) has emerged as a promising approach for identifying underlying physics when domain knowledge about observed data is limited. Despite recent progress, the identification of governing equations
Pongpisit Thanasutives, Yoshinobu Kawahara, Ken-ichi Fukui   +2 more
doaj   +1 more source

Bayesian model selection reveals biological origins of zero inflation in single-cell transcriptomics. [PDF]

Genome Biol, 2020
Choi K, Chen Y, Skelly DA, Churchill GA.
europepmc   +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

Bayesian Model Selection in the Analysis of Cointegration

, 2009
In this paper we present the Bayesian model selection procedure within the class of cointegrated processes. In order to make inference about the cointegration space we use the class of Matrix Angular Central Gaussian distributions. To carry out posterior
Wróblewska, Justyna
core  

Bayesian model selection in linear mixed models for longitudinal data. [PDF]

J Appl Stat, 2020
Ariyo O, Quintero A, Muñoz J, Verbeke G, Lesaffre E.   +4 more
europepmc   +1 more source

A Bayesian Model Selection Criterion for Selecting Pretraining Checkpoints

Accepted as an ICML 2025 ...
Michael Munn, Susan Wei
openaire   +3 more sources