1 citations · 1 across the 5 of their papers we have counts for
6 papers
Symplectic convolutional neural networks
Süleyman Yıldız, Konrad Janik, Peter Benner
We propose a new symplectic convolutional neural network (CNN) architecture by leveraging symplectic neural networks, proper symplectic decomposition, and tensor techniques. Specif…
A CFL-type Condition and Theoretical Insights for Discrete-Time Sparse Full-Order Model Inference
Leonidas Gkimisis, Süleyman Yıldız, Peter Benner +1
In this work, we investigate the data-driven inference of a discrete-time dynamical system via a sparse Full-Order Model (sFOM). We first formulate the involved Least Squares (LS)…
Structure-preserving learning for multi-symplectic PDEs
Süleyman Yıldız, Pawan Goyal, Peter Benner
This paper presents an energy-preserving machine learning method for inferring reduced-order models (ROMs) by exploiting the multi-symplectic form of partial differential equations…
Data-Driven Learning of Reduced-order Dynamics for a Parametrized Shallow Water Equation
Süleyman Yıldız, Pawan Goyal, Peter Benner +1
This paper discusses a non-intrusive data-driven model order reduction method that learns low-dimensional dynamical models for a parametrized shallow water equation. We consider th…
Reduced order modelling of nonlinear cross-diffusion systems
Bülent Karasözen, Gülden Mülayim, Murat Uzunca +1
In this work, we present a reduced-order model for a nonlinear cross-diffusion problem from population dynamics, for the Shigesada-Kawasaki-Teramoto (SKT) equation with Lotka-Volte…
Structure Preserving Model Order Reduction of Shallow Water Equations
Bülent Karasözen, Süleyman Yıldız, Murat Uzunca
In this paper, we present two different approaches for constructing reduced-order models (ROMs) for the two-dimensional shallow water equation (SWE). The first one is based on the…