Results 51 to 60 of about 215,493 (276)

Domination criticality in product graphs

AKCE International Journal of Graphs and Combinatorics, 2015
A connected dominating set is an important notion and has many applications in routing and management of networks. Graph products have turned out to be a good model of interconnection networks. This motivated us to study the Cartesian product of graphs G
M.R. Chithra, A. Vijayakumar
doaj   +1 more source

DyProL: Dynamic Ensemble Representation Learning for Protein–Nucleic Acid Binding Site Prediction

Advanced Science, EarlyView.
Protein function is represented as a dynamic conformational ensemble rather than a single static structure. A multi‐conformation geometric attention framework aligns, clusters, and learns representative states to capture residue‐ and ensemble‐level signals. Integrating structural dynamics improves interpretable protein‐NA binding prediction and reveals
Pengpai Li, Yiman Liu, Liya Liang, Rongming Liu   +3 more
wiley   +1 more source

Similarity‐Enhanced Representation Learning of Non‐Canonical Amino Acids for Therapeutic Peptide Modeling

Advanced Science, EarlyView.
Non‐canonical amino acids (ncAAs) enhance peptide therapeutics but remain difficult to model computationally. SinCAA, a similarity‐enhanced pretraining framework, jointly optimizes contrastive learning guided by a novel conformational similarity metric with masked node reconstruction, capturing both functional relationships and chemical identity of ...
Chencheng Xu, Lesong Wei, Jianmin Wang, Yuanpeng Xiong, Ruochi Zhang, Yu Wang, Chao Zha, Qiangcheng Zeng, Xin Gao   +8 more
wiley   +1 more source

On discontinuous Galerkin approach for atmospheric flow in the mesoscale with and without moisture

Meteorologische Zeitschrift, 2014
We present and discuss discontinuous Galerkin (DG) schemes for dry and moist atmospheric flows in the mesoscale. We derive terrain-following coordinates on the sphere in strong-conservation form, which makes it possible to perform the computation on a ...
Dieter Schuster, Slavko Brdar, Michael Baldauf, Andreas Dedner, Robert Klöfkorn, Dietmar Kröner   +5 more
doaj   +1 more source

Assessing Mesoscale Heterogeneities in Hard Carbon Electrodes Through Deep Learning‐Assisted FIB‐SEM Characterization, Manufacturing and Electrochemical Modeling

Advanced Energy Materials, EarlyView.
A combination of discrete and finite element method models for the current collector deformation and electrochemical performance analysis, respectively. The models are calibrated and validated with electrochemical and imaging data of hard carbon electrodes. These electrodes were manufactured with different parameters (slurry solid contents of 35 and 40
Soorya Saravanan, Imelda Cardenas‐Sierra, Pei Sun, Hari Raj, Daniele Duranti, Mahnoor Nafees, Edoardo Rossi, Marco Sebastiani, Paul Vigneaux, Martin Petit, Maxime Moreaud, Francisco Fernandez, Alejandro A. Franco   +12 more
wiley   +1 more source

A parallel methodology of adaptive Cartesian grid for compressible flow simulations

Advances in Aerodynamics, 2022
The combination of Cartesian grid and the adaptive mesh refinement (AMR) technology is an effective way to handle complex geometry and solve complex flow problems.
Xinyu Qi, Yuchen Yang, Linlin Tian, Zhenming Wang, Ning Zhao   +4 more
doaj   +1 more source

Evaluation of Flux Correction on Three-Dimensional Strand Grids with an Overset Cartesian Grid [PDF]

, 2017
Simulations of fluid flows over complex geometries are typically solved using a solution technique known as the overset meshing method. The geometry is meshed using grid types appropriate to the local geometry in a patchwork fashion, rather than meshing ...
Work, Dalon G.
core   +1 more source

Machine Learning Interatomic Potentials for Energy Materials: Architectures, Training Strategies, and Applications

Advanced Energy Materials, EarlyView.
Machine learning interatomic potentials bridge quantum accuracy and computational efficiency for materials discovery. Architectures from Gaussian process regression to equivariant graph neural networks, training strategies including active learning and foundation models, and applications in solid‐state electrolytes, batteries, electrocatalysts ...
In Kee Park, Saeed Pourasad, Jinhong Mun, Arunkumar Chitteth Rajan, Tae Hyeon Park, Anand Rohit, Mohammad Zafari, Tan‐Lien Pham, Eunjae Jung, Bonseung Koo, Miran Ha, Gi Beom Sim, Hyun Gyu Park, Won Woong Choi, Kwanghyeon Jo, Soohaeng Yoo Willow, David ChangMo Yang, Chang Woo Myung, Geunsik Lee, Kwang S. Kim   +19 more
wiley   +1 more source

High Order ADER Schemes for Continuum Mechanics

Frontiers in Physics, 2020
In this paper we first review the development of high order ADER finite volume and ADER discontinuous Galerkin schemes on fixed and moving meshes, since their introduction in 1999 by Toro et al.
Saray Busto, Simone Chiocchetti, Michael Dumbser, Elena Gaburro, Ilya Peshkov   +4 more
doaj   +1 more source

Why Physics Still Matters: Improving Machine Learning Prediction of Material Properties With Phonon‐Informed Datasets

Advanced Intelligent Discovery, EarlyView.
Phonons‐informed machine‐learning predictive models are propitious for reproducing thermal effects in computational materials science studies. Machine learning (ML) methods have become powerful tools for predicting material properties with near first‐principles accuracy and vastly reduced computational cost.
Pol Benítez, Cibrán López, Edgardo Saucedo, Teruyasu Mizoguchi, Claudio Cazorla   +4 more
wiley   +1 more source