Model reduction of dynamical systems on nonlinear manifolds using ...

We propose a novel framework for projecting dynamical systems onto nonlinear manifolds using minimum-residual formulations at the time-continuous and time- ...

[PDF] Model reduction of dynamical systems on nonlinear manifolds ...

Semantic Scholar extracted view of "Model reduction of dynamical systems on nonlinear manifolds using deep convolutional autoencoders" by Kookjin Lee et al.

(PDF) Model reduction of dynamical systems on nonlinear manifolds ...

Nearly all model-reduction techniques project the governing equations onto a linear subspace of the original state space. Such subspaces are typically ...

Model reduction of dynamical systems on nonlinear manifolds using ...

Nearly all model-reduction techniques project the governing equations onto a linear subspace of the original state space. Such subspaces are typically computed ...

Model reduction of dynamical systems on nonlinear manifolds using ...

Dive into the research topics of 'Model reduction of dynamical systems on nonlinear manifolds using deep convolutional autoencoders'. Together they form a ...

Learning physics-based reduced-order models from data using ...

We present a novel method for learning reduced-order models of dynamical systems using nonlinear manifolds. First, we learn the manifold by ...

model reduction | Kevin T. Carlberg

Model reduction of dynamical systems on nonlinear manifolds using deep convolutional autoencoders ... Nearly all model-reduction techniques project the governing ...

Preprint on model reduction on nonlinear manifolds using deep ...

... reduction using deep learning can be integrated in an optimal manner to reduce the dimensionality of nonlinear dynamical systems. Avatar ...

Learning physics-based reduced-order models from data using ...

Here we present a novel method for learning reduced-order models of dynamical systems using nonlinear manifolds. First, we learn the manifold by identifying ...

Fast data-driven model reduction for nonlinear dynamical systems

We present a fast method for nonlinear data-driven model reduction of dynamical systems onto their slowest nonresonant spectral submanifolds (SSMs).

Nonlinear model reduction from equations and data - AIP Publishing

With limited theoretical understanding of the underlying system, such nonlinear manifolds may be discovered by training autoencoders on time- ...

Symplectic Model Reduction of Hamiltonian Systems on Nonlinear ...

In this work, we bridge the two aforementioned approaches by providing a novel projection technique called symplectic manifold Galerkin (SMG), which projects ...

Non-linear Manifold Reduced-Order Models with Convolutional ...

The non-linear manifold method introduced by Carlberg et al. [4] does not perform a complete dimension reduction since at each time step the ...

Model Reduction for Nonlinear Dynamical Systems with Parametric ...

and [1], where ROM adaptation method based on interpolation in a tangent space to a Grassmann manifold was developed to 'correct' the precomputed ROMs to new.

An Online Manifold Learning Approach for Model Reduction of ...

iteration using reduced models (SIRM), for the dimensionality reduction of dynamical systems. ... White, Model order reduction for nonlinear dynamical systems ...

Operator inference for non-intrusive model reduction with quadratic ...

Our contribution in this paper is a novel reduced-order modeling framework for dynamical systems based on nonlinear solution-manifolds. The nonlinear mapping is ...

Model Reduction using Center and Inertial Manifolds

We review approaches for dimension reduction of smooth nonlinear dynamical systems. Here, the dimension of the state-space is reduced by projecting the system.

Nonlinear model reduction for dynamical systems using sparse ...

The synthesis of sparse sampling and dimensionality reduction to characterize and model nonlinear dynamical systems over a range of bifurcation parameters ...

Model Order Reduction of Nonlinear Dynamical Systems

The proposed manifold can be proven to capture important system dynamics such as DC and AC responses. We develop numerical methods that alleviates the ...

Symplectic model reduction of Hamiltonian systems on nonlinear ...

Thus, the reduced space needs to be extended to more general nonlinear manifolds. Moreover, as we are dealing with Hamiltonian systems, it is crucial that the ...