Upload Records Snowball Search Search OpenAlex
About the database ScholarIQanswers from OpenAlex
Model Reduction and Neural Networks
TopicLeading institutions, researchers & key papers
This cluster of papers focuses on the development and application of physics-informed neural networks for scientific computing, particularly in the context of solving partial differential equations, model reduction, fluid dynamics, dynamic mode decomposition, and nonlinear systems. The research explores the integration of deep learning techniques with traditional numerical methods to address complex problems in physics-based modeling and simulation.
27
Works
IDs:OpenAlex
How has Model Reduction and Neural Networks's publication output changed over time?
ScholarIQpublication output · 2013–2024
Output grew0% over the shown period — from 1 works in 2013 to 1 in 2024.
1
2
1
2
2
1
201320192020202220232024
What are the most-cited papers on Model Reduction and Neural Networks?
ScholarIQmost cited works
Fourier Neural Operator for Parametric Partial Differential Equations
Zongyi Li, Nikola Kovachki, Kamyar Azizzadenesheli, Burigede Liu, Kaushik Bhattacharya, Andrew M. Stuart, Anima Anandkumar
arXiv (Cornell University). 20201,095 CitationsOPEN ACCESS
Integrating machine learning and multiscale modeling—perspectives, challenges, and opportunities in the biological, biomedical, and behavioral sciences
Mark Alber, Adrián Buganza Tepole, William R. Cannon, Suvranu De, Salvador Durá-Bernal, Krishna Garikipati, George Em Karniadakis, William W. Lytton, Paris Perdikaris, Linda Petzold, Ellen Kuhl
npj Digital Medicine. 2019636 CitationsOPEN ACCESS
U-FNO—An enhanced Fourier neural operator-based deep-learning model for multiphase flow
Gege Wen, Zongyi Li, Kamyar Azizzadenesheli, Anima Anandkumar, Sally M. Benson
S145524021. 2022496 CitationsOPEN ACCESS
Neural operators for accelerating scientific simulations and design
Kamyar Azizzadenesheli, Nikola Kovachki, Zongyi Li, Miguel Liu-Schiaffini, Jean Kossaifi, Anima Anandkumar
S4210234670. 2024259 Citations
Group-wise construction of reduced models for understanding and characterization of pulmonary blood flows from medical images
Romain Guibert, Kristin McLeod, Alfonso Caiazzo, Tommaso Mansi, Miguel Á. Fernández, Maxime Sermesant, Xavier Pennec, Irène Vignon-Clémentel, Younès Boudjemline, Jean-Frédéric Gerbeau
S116571295. 201337 CitationsOPEN ACCESS
Where is Model Reduction and Neural Networks research published, and who funds it?
ScholarIQvenues & funding sources
TOP JOURNALS
TOP FUNDERS
National Science Foundation—
NIH—
Wellcome Trust—
European Research Council—
Funder breakdown is a member featureSign up free to unlock
How much of the research on Model Reduction and Neural Networks is open access?
ScholarIQopen access share
89%OPEN ACCESS
Gold
11%
Green
67%
Hybrid
0%
Bronze
11%
Closed
11%
Related on ScholarIQ
Score-Based Generative Modeling through Stochastic Differential Equations
Paper
Fourier Neural Operator for Parametric Partial Differential Equations
Paper
Physics-informed machine learning: case studies for weather and climate modelling
Paper
Integrating machine learning and multiscale modeling—perspectives, challenges, and opportunities in the biological, biomedical, and behavioral sciences
Paper
U-FNO—An enhanced Fourier neural operator-based deep-learning model for multiphase flow
Paper
Multiscale modeling meets machine learning: What can we learn?
Paper