Results 161 to 170 of about 108,134 (338)

A multiscale Bayesian optimization framework for process and material codesign

AIChE Journal, EarlyView.
Abstract The simultaneous design of processes and enabling materials such as solvents, catalysts, and adsorbents is challenging because molecular‐ and process‐level decisions are strongly interdependent. Sequential approaches often yield suboptimal results since improvements in material properties may not translate into superior process performance. We
Michael Baldea
wiley   +1 more source

A Comparative Study of Sea Clutter Covariance Matrix Estimators

, 2013
Tao Ding, Stian Normann Anfinsen, Camilla Brekke   +2 more
openalex   +2 more sources

Geometric sensitivity of random matrix results: consequences for shrinkage estimators of covariance and related statistical methods [PDF]

, 2011
Noureddine El Karoui, Holger Koesters
openalex   +1 more source

Real‐time fault detection in multicomponent nuclear‐waste slurries through data fusion of spectroscopic sensors

AIChE Journal, EarlyView.
Abstract Three instruments–Raman spectroscopy, attenuated total reflectance–Fourier transform infrared spectroscopy, and focused beam reflectance measurement–were used to detect sensor faults, mixing faults, and unanticipated chemistry in a system of multicomponent slurries.
Steven H. Crouse, Ronald W. Rousseau, Martha A. Grover   +2 more
wiley   +1 more source

Trust‐region filter algorithms utilizing Hessian information for gray‐box optimization

AIChE Journal, EarlyView.
Abstract Optimizing industrial processes often involves gray‐box models that couple algebraic glass‐box equations with black‐box components lacking analytic derivatives. Such systems challenge derivative‐based solvers. The classical trust‐region filter (TRF) algorithm provides a robust framework but requires extensive parameter tuning and numerous ...
Gul Hameed, Tao Chen, Antonio del Rio Chanona, Lorenz T. Biegler, Michael Short   +4 more
wiley   +1 more source

Heteroskedasticity-consistent covariance matrix estimators for spatial autoregressive models

, 2018
Süleyman Taşpınar, Osman Doğan, Anil K. Bera   +2 more
openalex   +2 more sources

Fluctuations of an Improved Population Eigenvalue Estimator in Sample Covariance Matrix Models [PDF]

, 2012
Jianfeng Yao, Romain Couillet, Jamal Najım, Mérouane Debbah   +3 more
openalex   +1 more source

Sequential Monte Carlo with likelihood tempering and parallel implementation for uncertainty quantification

AIChE Journal, EarlyView.
Abstract Bayesian estimation enables uncertainty quantification, but analytical implementation is often intractable. As an approximate approach, the Markov Chain Monte Carlo (MCMC) method is widely used, though it entails a high computational cost due to frequent evaluations of the likelihood function.
Tatsuki Maruchi, Tomoyuki Yajima, Yoshiaki Kawajiri   +2 more
wiley   +1 more source

Domain‐Aware Implicit Network for Arbitrary‐Scale Remote Sensing Image Super‐Resolution

Advanced Intelligent Discovery, EarlyView.
Although existing arbitrary‐scale image super‐resolution methods are flexible to reconstruct images with arbitrary scales, the characteristic of training distribution is neglected that there exists domain shift between samples of various scales. In this work, a Domain‐Aware Implicit Network (DAIN) is proposed to handle it from the perspective of domain
Xiaoxuan Ren, Qian Jiang, Xin Jin, Puming Wang, Xing Chu, Shaowen Yao, Shengfa Miao   +6 more
wiley   +1 more source

An Amplitude And Covariance Matrix Estimator For Signals In Colored Gaussian Noise

, 2009
Mads Græsbøll Christensen, Jesper Højvang Jensen, Søren Holdt Jensen   +2 more
openalex   +1 more source