Search Results for author:
Found 6 papers, 0 papers with code
State-Of-The-Art Algorithms For Low-Rank Dynamic Mode Decomposition
This technical note reviews sate-of-the-art algorithms for linear approximation of high-dimensional dynamical systems using low-rank dynamic mode decomposition (DMD).
Generalized Kernel-Based Dynamic Mode Decomposition
Reduced modeling in high-dimensional reproducing kernel Hilbert spaces offers the opportunity to approximate efficiently non-linear dynamics.
Low-rank Approximation of Linear Maps
The theorem provides the basis for the design of tractable algorithms for kernel or continuous DMD.
Sea surface temperature prediction and reconstruction using patch-level neural network representations
The forecasting and reconstruction of ocean and atmosphere dynamics from satellite observation time series are key challenges.
Bilinear residual Neural Network for the identification and forecasting of dynamical systems
Due to the increasing availability of large-scale observation and simulation datasets, data-driven representations arise as efficient and relevant computation representations of dynamical systems for a wide range of applications, where model-driven models based on ordinary differential equation remain the state-of-the-art approaches.
Elastic Shape-From-Template With Spatially Sparse Deforming Forces
We prove that filling this property is necessary and sufficient for the relaxed formulation to: (i) retrieve the ground-truth 3D deformed shape, (ii) recover the right spatial domain of non-zero deforming forces.
