Results 201 to 210 of about 1,581,096 (371)

On the Theory of Kernels of Schwartz [PDF]

, 1956
Leon Ehrenpreis
openalex   +1 more source

Correspondence of NNGP Kernel and the Matern Kernel

Kernels representing limiting cases of neural network architectures have recently gained popularity. However, the application and performance of these new kernels compared to existing options, such as the Matern kernel, is not well studied. We take a practical approach to explore the neural network Gaussian process (NNGP) kernel and its application to ...
Muyskens, Amanda, Priest, Benjamin W., Goumiri, Imene R., Schneider, Michael D.   +3 more
openaire   +2 more sources

Etherification of Bio‐Based Furanic Compounds via Continuous Flow

Advanced Sustainable Systems, EarlyView.
A comprehensive investigation on the etherification of bio‐based furanic compounds using a continuous flow apparatus. Reaction conditions are optimized for the synthesis of BEMF and then used to obtain a library of BAMFs. Additionally, the self‐etherification of HMF to produce OBMF is explored both in heterogeneous and by employing the homogeneous ...
Davide Dalla Torre, Giacomo Trapasso, Nathan Jourdainne, Nathanael Guigo, Fabio Aricò   +4 more
wiley   +1 more source

Damages to Rice Crops Caused by a Wind Storm, with Special Reference to a Typhoon that Visited Kansai District, Japan. Part IV. Investigations of Rice Kernels of Damaged Grains

, 1940
Yosizi TOGARI
openalex   +2 more sources

The distribution of vitamin-B in the wheat kernel [PDF]

, 1922
Matthew Bell, Lafayette B. Mendel
openalex   +1 more source

Accurate and Efficient Behavioral Modeling of GaN HEMTs Using An Optimized Light Gradient Boosting Machine

Advanced Theory and Simulations, EarlyView.
Machine Learning (ML) and optimization have permeated almost every aspect of engineering applications. Recent years have seen great traction toward ML‐based GaN HEMT modelling. However, ML‐based GaN HEMT models are mostly developed using variants of Artificial Neural Network (ANN).
Saddam Husain, Mohammad Hashmi, Fadhel M. Ghannouchi   +2 more
wiley   +1 more source

Predicting Fiber Length Characteristics of Recycled Cotton and Cellulose Fiber Blends Using Machine Learning Models

Advanced Theory and Simulations, EarlyView.
This study explores machine learning‐driven prediction of fiber length characteristics in sustainable yarn blends made from recycled cotton and Lyocell. By analyzing empirical data through models like Random Forest and Gradient Boosting, and interpreting results with SHAP, key fiber length features from the Staple Diagram and Fibrogram are identified ...
Tuser Tirtha Biswas, Michael Will, Nawar Kadi   +2 more
wiley   +1 more source

Cloning and function analysis of <i>ZmICE1a</i>, a contributor to the melioration of maize kernel traits. [PDF]

Plant Signal Behav
Xiao Y, Tu L, Jiang Y, Liu P, Guo X, Wang A, Zhu Y, Lu X, Chen Z, Wu X.   +9 more
europepmc   +1 more source

Excitation Energy Transfer between Porphyrin Dyes on a Clay Surface: A Study Employing Multifidelity Machine Learning

Advanced Theory and Simulations, EarlyView.
Inspired by natural light‐harvesting systems, this study computationally investigates a synthetic antenna by arranging cationic free‐base porphyrin molecules on an anionic clay surface. Using a multiscale quantum mechanics/molecular mechanics (QM/MM) approach combined with a multifidelity machine learning method, excitation energies are predicted ...
Dongyu Lyu, Vivin Vinod, Matthias Holzenkamp, Yannick Marcel Holtkamp, Sayan Maity, Carlos R. Salazar, Ulrich Kleinekathöfer, Peter Zaspel   +7 more
wiley   +1 more source

Leveraging Transfer Learning to Overcome Data Limitations in Czochralski Crystal Growth

Advanced Theory and Simulations, EarlyView.
A data‐driven framework combining Computational Fluid Dynamics (CFD) simulations and machine learning is proposed to model and optimize Czochralski crystal growth. Using different transfer learning strategies (Warm Start, Merged Training, and Hyperparameter Transfer) the study demonstrates improved predictions for Ge and GaAs growth from Si‐trained ...
Milena Petkovic, Natasha Dropka, Xia Tang, Janina Zittel   +3 more
wiley   +1 more source