Results 191 to 200 of about 3,567,722 (263)

Harnessing machine learning and optimization for informed chemical engineering decisions: A styrene reactor analysis

open access: yesThe Canadian Journal of Chemical Engineering, EarlyView.
This study shows that integrating multiple machine learning models with optimization and decision‐making improves chemical process design, and that a consensus‐based strategy across models provides more robust and reliable operating recommendations than any single model, especially under limited or noisy data conditions.
Farough Agin   +2 more
wiley   +1 more source

Lasso for hierarchical polynomial models

open access: yesCanadian Journal of Statistics, EarlyView.
Abstract The divisibility conditions implicit in a polynomial hierarchy suggest parameter constraints in regression. With this idea, we establish strong and weak hierarchies for both the lasso and relaxed lasso. Our proposal extends prior work on hierarchical lasso, which was mainly concerned with models of degree 2.
H. Maruri‐Aguilar, S. Lunagómez
wiley   +1 more source

Mitigating measurement error in misspecified small area models

open access: yesCanadian Journal of Statistics, EarlyView.
Abstract In the framework of Small area estimation, we consider an area‐level model where a subset of covariates is measured with error. The extent of the error is assumed to be constant throughout the areas, and it is expressed by a scalar parameter γ$$ \gamma $$, which multiplies the deterministic covariance matrix of the estimator of the true ...
Diego Battagliese   +3 more
wiley   +1 more source

Front Propagation Through a Perforated Wall

open access: yesCommunications on Pure and Applied Mathematics, EarlyView.
ABSTRACT We consider a bistable reaction– diffusion equation ut=Δu+f(u)$u_t=\Delta u +f(u)$ on RN${\mathbb {R}}^N$ in the presence of an obstacle K$K$, which is a wall of infinite span with many holes. More precisely, K$K$ is a closed subset of RN${\mathbb {R}}^N$ with smooth boundary such that its projection onto the x1$x_1$‐axis is bounded and that ...
Henri Berestycki   +2 more
wiley   +1 more source

Generalized Matsuoka–Nakai criterion considering hydrostatic pressure dependence in the brittle–ductile region

open access: yesDeep Underground Science and Engineering, EarlyView.
Three sets of strength data were selected, including hydrostatic pressure independent within the brittle region (HPI‐B), hydrostatic pressure dependent within the brittle region (HPD‐B), and hydrostatic pressure dependent within the brittle–ductile region (HPD‐BD). For HPI type, the failure envelope within the deviatoric plane remains constant.
Jiacun Liu   +3 more
wiley   +1 more source

Home - About - Disclaimer - Privacy