Results 91 to 100 of about 1,839 (214)

On highly oscillatory problems arising in electronic engineering [PDF]

, 2003
In this paper, we consider linear ordinary differential equations originating in electronic engineering, which exhibit exceedingly rapid oscillation.
Deaño Cabrera, Alfredo, Roland Becker, Larson, Mats G, Hansbo, Anita,, Osses Alvarado, Axel, Kinderlehrer, David, Stenberg, Rolf, Puel, Jean-Pierre, Kowalczyk, Michal, Garcia, Galina, Stefano Berrone, Knobloch, Petr, Axel Osses, Michal Kowalczyk, García, Galina C., Hansbo, Peter F G, Hansbo, Peter, Lisini, Stefano, John, Volker, Puel, Jean Pierre, Barrenechea, Gabriel, Michał Kowalczyk, Gabriel R. Barrenechea, Petr Knobloch, Stenberg, Rolf,, Hansbo, Peter,, Kropielnicka, Karolina, Arieh Iserles, Carrillo, José Antonio, Rolf Stenberg, Larson, Mats, Anita Hansbo, Larson, Mats,, Berrone, Stefano, Osses, Axel, Hansbo, Peter F G,, Laurençot, Philippe, Karolina Kropielnicka, Volker John, Alfredo Deaño, Deaño, Alfredo, Jean Pierre Puel, Galina C. García, Kowalczyk, Michał, Carrillo, José, A., Philippe Laurençot, Larson, Mats G., Atulya K. Nagar, Barrenechea, Gabriel R., Adrien Blanchet, Stefano Lisini, Hansbo, Anita, Volker, John, Carrillo, José, Iserles, Arieh, Peter Hansbo, Becker, Roland,, Condon, Marissa, Becker, Roland, GIRI, ANKIK KUMAR, Ankik Kumar Giri, Nagar, Atulya K., Blanchet, Adrien, Marissa Condon, Mats G. Larson, David Kinderlehrer, José Antonio Carrillo   +66 more
core   +1 more source

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

Parallel computing using the multiscale finite element method for sub-surface flow models [PDF]

, 2016
Subsurface flows, occurring in groundwater movement and production of hydrocarbons in the petroleum industry, are affected by the heterogeneity of the medium varying over large scales.
Goyal, Anshul
core   +1 more source

Enhanced Technique for Metascreens Using the Generalized Finite Element Method

, 2021
An efficient approach to simulate the multiple scales, occurring in metascreens, is introduced. Due to the fine discretization around apertures, the standard finite element (FE) method leads to a large number of unknowns.
Schöberl, Joachim, Leumüller, Michael, Hollaus, Karl; orcid:, Auinger, Bernhard; orcid:   +3 more
core   +1 more source

Advances in Thermal Modeling and Simulation of Lithium‐Ion Batteries with Machine Learning Approaches

Advanced Intelligent Discovery, EarlyView.
Heat generation in lithium‐ion batteries affects performance, aging, and safety, requiring accurate thermal modeling. Traditional methods face efficiency and adaptability challenges. This article reviews machine learning‐based and hybrid modeling approaches, integrating data and physics to improve parameter estimation and temperature prediction ...
Qi Lin, Kang Fu, Junjie Ding, Kai Sun, Peng Tan   +4 more
wiley   +1 more source

Generalized multiscale finite element methods for space–time heterogeneous parabolic equations

Computers & Mathematics with Applications, 2018
In this paper, we consider local multiscale model reduction for problems with multiple scales in space and time. We developed our approaches within the framework of the Generalized Multiscale Finite Element Method (GMsFEM) using space-time coarse cells. The main idea of GMsFEM is to construct a local snapshot space and a local spectral decomposition in
Chung, Eric T., Efendiev, Yalchin R., Leung, Wing Tat, Ye, Shuai   +3 more
openaire   +5 more sources

Sampling Strategy: An Overlooked Factor Affecting Artificial Intelligence Prediction Accuracy of Peptides’ Physicochemical Properties

Advanced Intelligent Discovery, EarlyView.
This study reveals that sampling strategy (i.e., sampling size and approach) is a foundational prerequisite for building accurate and generalizable AI models in peptide discovery. Reaching a threshold of 7.5% of the total tetrapeptide sequence space was essential to ensure reliable predictions.
Meiru Yan, Ankeer Abuduhebaier, Haojin Zhou, Jiaqi Wang   +3 more
wiley   +1 more source

Interpretability and Representability of Commutative Algebra, Algebraic Topology, and Topological Spectral Theory for Real‐World Data

Advanced Intelligent Discovery, EarlyView.
This article investigates how persistent homology, persistent Laplacians, and persistent commutative algebra reveal complementary geometric, topological, and algebraic invariants or signatures of real‐world data. By analyzing shapes, synthetic complexes, fullerenes, and biomolecules, the article shows how these mathematical frameworks enhance ...
Yiming Ren, Guo‐Wei Wei
wiley   +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

Autonomous AI‐Driven Design for Skin Product Formulations

Advanced Intelligent Discovery, EarlyView.
This review presents a comprehensive closed‐loop framework for autonomous skin product formulation design. By integrating artificial intelligence‐driven experiment selection with automated multi‐tiered assays, the approach shifts development from trial‐and‐error to intelligent optimisation.
Yu Zhang, Yongrui Xiao, Chunlin Chen, Dimitrios Tsaoulidis, Kai Yang, Tao Chen   +5 more
wiley   +1 more source