Model Reduction and Neural Networks
Description: 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.
Characteristic keywords: model reduction, physics-informed neural networks, proper orthogonal decomposition, dynamic mode decomposition, neural operators, physics-informed machine learning, reduced basis method, Koopman operator, Fourier neural operator, Galerkin projection, DeepONet, proper generalized decomposition, operator learning, scientific machine learning, flow reconstruction, neural ordinary differential equations, nonlinear model reduction, discrete empirical interpolation method, balanced truncation, hyper-reduction, parametric partial differential equations, data-driven model reduction, spectral bias, sparse identification of nonlinear dynamics, extended dynamic mode decomposition
Sibling topics: Complex Network Analysis Techniques, Chaos control and synchronization, Nonlinear Waves and Solitons, Nonlinear Dynamics and Pattern Formation, Quantum chaos and dynamical systems, stochastic dynamics and bifurcation, Advanced Thermodynamics and Statistical Mechanics, Nonlinear Photonic Systems, Statistical Mechanics and Entropy, Advanced Mathematical Theories and Applications, Opinion Dynamics and Social Influence, Fusion and Plasma Physics Studies, Complex Systems and Dynamics
Parent subfield: Statistical and Nonlinear Physics
Field: Physics and Astronomy
Domain: Physical Sciences
OpenAlex ID: T11206