Model Reduction and Neural Networks

Model Reduction and Neural Networks is a research topic within Statistical and Nonlinear Physics. Science Explorer counts 25k research works in it since 1950. 9.9% of them reached the world's top 10% most cited for their field and year.

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.

• Deep Learning
• Partial Differential Equations
• Model Reduction
• Fluid Dynamics
• Dynamic Mode Decomposition
• Nonlinear Systems
• Machine Learning
• Data-Driven Modeling
• Numerical Computing
• Inverse Problems