Search Results for author:
Found 4 papers, 4 papers with code
Lagrangian PINNs: A causality-conforming solution to failure modes of physics-informed neural networks
A parallel architecture with two branches is proposed.
Physics-aware registration based auto-encoder for convection dominated PDEs
We design a physics-aware auto-encoder to specifically reduce the dimensionality of solutions arising from convection-dominated nonlinear physical systems.
Deep convolutional recurrent autoencoders for learning low-dimensional feature dynamics of fluid systems
In this work we propose a deep learning-based strategy for nonlinear model reduction that is inspired by projection-based model reduction where the idea is to identify some optimal low-dimensional representation and evolve it in time.
Lagrangian basis method for dimensionality reduction of convection dominated nonlinear flows
Foundations of a new projection-based model reduction approach for convection dominated nonlinear fluid flows are summarized.
Fluid Dynamics
