Results 201 to 210 of about 880,429 (326)

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

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

Deciphering the Physiological Responses to the Intake of Plant-Based Meat Analogues: On the Track of Microbiota and Biomarkers in Serum and Urine. [PDF]

J Agric Food Chem
Sánchez-Terrón G, Martínez R, Morcuende D, Estévez M.   +3 more
europepmc   +1 more source

Particle filled protein-starch composites as the basis for plant-based meat analogues. [PDF]

Curr Res Food Sci, 2022
Dobson S, Laredo T, Marangoni AG.
europepmc   +1 more source

AI in chemical engineering: From promise to practice

AIChE Journal, EarlyView.
Abstract Artificial intelligence (AI) in chemical engineering has moved from promise to practice: physics‐aware (gray‐box) models are gaining traction, reinforcement learning complements model predictive control (MPC), and generative AI powers documentation, digitization, and safety workflows.
Jia Wei Chew, Ronnie Andersson, Thomas Bierweiler, Patrik Ryttestal, Torsten Wik   +4 more
wiley   +1 more source

Mycotoxins and Other Secondary Metabolites Produced in vitro by Penicillium paneum Frisvad and Penicillium roqueforti Thom Isolated from Baled Grass Silage in Ireland [PDF]

, 2006
Auerbach H., Bentley R., Boysen M., Boysen M. E., Cole R. J., DiCostanzo A., Dombrink-Kurtzman M. A., Fink-Gremmels J., Frisvad J. C., Frisvad J. C., Frisvad J. C., Garon D., Häggblom P, Kopp-Holtwiesche B., Lacey J, Moreau S., Moulé Y., Moulé Y., Müller H. M., Nielsen K. F., Nielsen K. F., Nielsen K. F., Nielsen K. F., O'Brien M., O'Brien M., O'Brien M., O'Brien M., O'Kiely P., Pelhate J., Pitt J. I., Polonelli L., Rand T. G., Rho M. C., Samson R. A., Samson R. A., Santos I. M., Schneweis I., Scott R. E., Smedsgaard J, Smith D. F., Still P. E., Sumarah M. W., Tapia M. O., Tüller G., Ueno Y., Vesely D.   +45 more
core   +2 more sources

Exploring Quantum Support Vector Regression for Predicting Hydrogen Storage Capacity of Nanoporous Materials

Advanced Intelligent Discovery, EarlyView.
In this study we employed support vector regressor and quantum support vector regressor to predict the hydrogen storage capacity of metal–organic frameworks using structural and physicochemical descriptors. This study presents a comparative analysis of classical support vector regression (SVR) and quantum support vector regression (QSVR) in predicting ...
Chandra Chowdhury
wiley   +1 more source

Feature Selection for Machine Learning‐Driven Accelerated Discovery and Optimization in Emerging Photovoltaics: A Review

Advanced Intelligent Discovery, EarlyView.
Feature selection combined with machine learning and high‐throughput experimentation enables efficient handling of high‐dimensional datasets in emerging photovoltaics. This approach accelerates material discovery, improves process optimization, and strengthens stability prediction, while overcoming challenges in data quality and model scalability to ...
Jiyun Zhang, Jiayi Tan, Qizhen Song, Tian Du, Jens Hauch, Christoph J. Brabec   +5 more
wiley   +1 more source

Plant Taxa as Raw Material in Plant-Based Meat Analogues (PBMAs)-A Patent Survey. [PDF]

Nutrients
Kostrakiewicz-Gierałt K.
europepmc   +1 more source

What to Make and How to Make It: Combining Machine Learning and Statistical Learning to Design New Materials

Advanced Intelligent Discovery, EarlyView.
Combining machine learning and probabilistic statistical learning is a powerful way to discover and design new materials. A variety of machine learning approaches can be used to identify promising candidates for target applications, and causal inference can help identify potential ways to make them a reality.
Jonathan Y. C. Ting, Amanda S. Barnard
wiley   +1 more source