Model Reduction and Neural Networks

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.

Journals publishing in this area

1

Journal of Computational Physics

ISSN 0021-99911,452 articles in this topic

411h-index

3.35Impact

21.1KArticles

1.3MCitations

2

Computer Methods in Applied Mechanics and Engineering

ISSN 0045-78251,210 articles in this topic

313h-index

7.04Impact

16.7KArticles

817.6KCitations

3

Physics of Fluids

ISSN 1070-6631946 articles in this topic

242h-index

3.26Impact

34.1KArticles

762KCitations

4

IEEE Transactions on Automatic Control

ISSN 0018-9286855 articles in this topic

474h-index

3.89Impact

26.9KArticles

1.5MCitations

5

International Journal for Numerical Methods in Engineering

ISSN 0029-5981585 articles in this topic

270h-index

2.32Impact

15.4KArticles

568.2KCitations

6

Automatica

ISSN 0005-1098465 articles in this topic

397h-index

4.20Impact

15.6KArticles

1.1MCitations

7

Neural Networks

ISSN 0893-6080302 articles in this topic

246h-index

4.61Impact

9.8KArticles

469.1KCitations

8

IEEE Transactions on Neural Networks

ISSN 1045-9227136 articles in this topic

265h-index

3.8KArticles

465.3KCitations

9

Neural Computation

ISSN 0899-7667108 articles in this topic

247h-index

2.31Impact

3.8KArticles

491.1KCitations