Results 171 to 180 of about 1,054,196 (331)

Approximation properties of periodic multivariate quasi-interpolation operators [PDF]

Journal of Approximation Theory, 2020
Yurii Kolomoitsev, Jürgen Prestin
semanticscholar   +1 more source

Accelerating Surface Composition Characterization of Thin‐Film Materials Libraries Using Multi‐Output Gaussian Process Regression

Advanced Intelligent Discovery, EarlyView.
To integrate surface analysis into materials discovery workflows, Gaussian process regression is used to accurately predict surface compositions from rapidly acquired volume composition data (obtained by energy‐dispersive X‐ray spectroscopy), drastically reducing the number of required surface measurements on thin‐film materials libraries.
Felix Thelen, Florian Lourens, Alfred Ludwig   +2 more
wiley   +1 more source

Elucidating Ligand‐Dependent Selectivities in Pd‐Catalyzed C–H Activations

Angewandte Chemie, EarlyView.
Strategies to understand ligand effects on regioselectivities of C–H activation processes were investigated using an alkynylation of thiophenes as a model system. The results shed light on the role of dispersion models for the correct prediction of reaction outcomes and propose new roles for the solvent and silver additive.
Fritz Deufel, Monika Ravi, Manuel van Gemmeren   +2 more
wiley   +2 more sources

Application of multivariate statistics and geostatistical techniques to identify the spatial variability of heavy metals in groundwater resources

Caspian Journal of Environmental Sciences, 2015
The performance of geostatistical and spatial interpolation techniques were investigated for estimation of spatial variability of heavy metals and water quality mapping of groundwater resources in Ramiyan district (Golestan province, Iran).
Khanduzi, F., Parizanganeh, A., Zamani, A.   +2 more
doaj  

Inverse Design of Alloys via Generative Algorithms: Optimization and Diffusion within Learned Latent Space

Advanced Intelligent Discovery, EarlyView.
This work presents a novel generative artificial intelligence (AI) framework for inverse alloy design through operations (optimization and diffusion) within learned compact latent space from variational autoencoder (VAE). The proposed work addresses challenges of limited data, nonuniqueness solutions, and high‐dimensional spaces.
Mohammad Abu‐Mualla, Ellis Crabtree, Fredrick Michael, Yayue Pan, Jida Huang   +4 more
wiley   +1 more source

Materials Innovation and the Changing Face of Photocatalytic and Electrocatalytic Carbon Dioxide Reduction Research: From Metal Nanoclusters to Extended Frameworks

Angewandte Chemie, EarlyView.
Innovative catalyst design is advancing precision‐controlled catalysis for CO2 reduction. This minireview investigates the forefront of photocatalytic and electrocatalytic CO2 reduction (CO2RR) leveraging metal nanoclusters (NCs), metal–organic frameworks (MOFs), and covalent organic frameworks (COFs), concentrating on structure–activity correlations ...
Tsukasa Irie, Kohki Sasaki, Saikat Das, Yuichi Negishi   +3 more
wiley   +2 more sources

Deep Learning‐Assisted Design of Mechanical Metamaterials

Advanced Intelligent Discovery, EarlyView.
This review examines the role of data‐driven deep learning methodologies in advancing mechanical metamaterial design, focusing on the specific methodologies, applications, challenges, and outlooks of this field. Mechanical metamaterials (MMs), characterized by their extraordinary mechanical behaviors derived from architected microstructures, have ...
Zisheng Zong, Pu Wen, Zhiping Chai, Dunxuan Wu, Han Ding, Zhigang Wu   +5 more
wiley   +1 more source

Adapting Image‐Based Models for 1D Data via Spider Plot Transformation and Transfer Learning

Advanced Intelligent Systems, EarlyView.
A novel method enables the use of pretrained image‐based neural networks for complex 1D data, including Raman and mid‐infrared spectra, electrocardiograms, and mass spectrometry. 2D spider plots with false‐color fill enable transfer lerning, therefore enhancing data augmentation and model explainability across diverse spectral and time series datasets.
Azadeh Mokari, Oleg Ryabchykov, Thomas Bocklitz   +2 more
wiley   +1 more source

Multivariate Polynomial Interpolation to Traces on Manifolds

, 1995
Hakop Hakopian, Artur Sahakian
openalex   +1 more source

Riemannian Geometry for the Classification of Brain States with Intracortical Brain Recordings

Advanced Intelligent Systems, EarlyView.
Geometric machine learning is applied to decode brain states from invasive intracortical neural recordings, extending Riemannian methods to the invasive regime where data is scarcer and less stationary. A Minimum Distance to Mean classifier on covariance manifolds uses geodesic distances to outperform convolutional neural networks while reducing ...
Arnau Marin‐Llobet, Sergio Sánchez‐Manso, Arnau Manasanch, Lluc Tresserras, Xinhe Zhang, Yining Hua, Hao Zhao, Melody Torao‐Angosto, Maria V Sanchez‐Vives, Leonardo Dalla Porta   +9 more
wiley   +1 more source