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 +8 more
wiley +1 more source
A Note on N in Bayesian Information Criterion for Item Response Models. [PDF]
Appl Psychol Meas, 2018 Cho SJ, De Boeck P.
europepmc +1 more source
On the overestimation of widely applicable Bayesian information criterion
, 2019 A widely applicable Bayesian information criterion (Watanabe, 2013) is applicable for both regular and singular models in the model selection problem. This criterion tends to overestimate the log marginal likelihood. We identify an overestimating term of a widely applicable Bayesian information criterion.
openaire +2 more sources
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 +3 more
wiley +1 more source
Akaike's Bayesian Information Criterion for the Joint Inversion of Terrestrial Water Storage Using GPS Vertical Displacements, GRACE and GLDAS in Southwest China. [PDF]
Entropy (Basel), 2019 Liu Y, Fok HS, Tenzer R, Chen Q, Chen X.
europepmc +1 more source
Sustainable Materials Design With Multi‐Modal Artificial Intelligence
Advanced Science, EarlyView.Critical mineral scarcity, high embodied carbon, and persistent pollution from materials processing intensify the need for sustainable materials design. This review frames the problem as multi‐objective optimization under heterogeneous, high‐dimensional evidence and highlights multi‐modal AI as an enabling pathway.
Tianyi Xu +8 more
wiley +1 more source
A business process clustering algorithm using incremental covering arrays to explore search space and balanced Bayesian information criterion to evaluate quality of solutions. [PDF]
PLoS One, 2019 Ordoñez H +5 more
europepmc +1 more source
Advanced Science, EarlyView.By overcoming the fixed‐path limitations of conventional machine learning, a heterogeneous graph neural network fundamentally reconstructs material data representation. Integrating variable processing sequences with intrinsic elemental features, this framework enables exploratory optimization across high‐dimensional spaces.
Jie Yin +12 more
wiley +1 more source
Adaptive modelling of gene regulatory network using Bayesian information criterion-guided sparse regression approach. [PDF]
IET Syst Biol, 2016 Shi M, Shen W, Wang HQ, Chong Y.
europepmc +1 more source
Mixtures of g-priors for Bayesian Model Averaging with economic application [PDF]
This paper examines the issue of variable selection in linear regression modeling, where there is a potentially large amount of possible covariates and economic theory offers insufficient guidance on how to select the appropriate subset. In this context,
Ley, Eduardo, Steel, Mark F.J.
core