Bayesian operator inference for data-driven reduced-order modeling
This work proposes a Bayesian inference method for the reduced-order modeling of time-
dependent systems. Informed by the structure of the governing equations, the task of …
dependent systems. Informed by the structure of the governing equations, the task of …
Guaranteed stable quadratic models and their applications in SINDy and operator inference
Scientific machine learning for learning dynamical systems is a powerful tool that combines
data-driven modeling models, physics-based modeling, and empirical knowledge. It plays …
data-driven modeling models, physics-based modeling, and empirical knowledge. It plays …
Port-Hamiltonian dynamic mode decomposition
R Morandin, J Nicodemus, B Unger - SIAM Journal on Scientific Computing, 2023 - SIAM
We present a novel physics-informed system identification method to construct a passive
linear time-invariant system. In more detail, for a given quadratic energy functional …
linear time-invariant system. In more detail, for a given quadratic energy functional …
Physics-informed regularization and structure preservation for learning stable reduced models from data with operator inference
N Sawant, B Kramer, B Peherstorfer - Computer Methods in Applied …, 2023 - Elsevier
Operator inference learns low-dimensional dynamical-system models with polynomial
nonlinear terms from trajectories of high-dimensional physical systems (non-intrusive model …
nonlinear terms from trajectories of high-dimensional physical systems (non-intrusive model …
On bilinear time-domain identification and reduction in the Loewner framework
The Loewner framework (LF) in combination with Volterra series (VS) offers a non-intrusive
approximation method that is capable of identifying bilinear models from time-domain …
approximation method that is capable of identifying bilinear models from time-domain …
Data-driven modeling of hypersonic reentry flow with heat and mass transfer
The entry phase constitutes a design driver for aerospace systems that include such a
critical step. This phase is characterized by hypersonic flows encompassing multiscale …
critical step. This phase is characterized by hypersonic flows encompassing multiscale …
Structure-preserving hyper-reduction and temporal localization for reduced order models of incompressible flows
RB Klein, B Sanderse - arXiv preprint arXiv:2304.09229, 2023 - arxiv.org
A novel hyper-reduction method is proposed that conserves kinetic energy and momentum
for reduced order models of the incompressible Navier-Stokes equations. The main …
for reduced order models of the incompressible Navier-Stokes equations. The main …
A quadratic decoder approach to nonintrusive reduced‐order modeling of nonlinear dynamical systems
Linear projection schemes like Proper Orthogonal Decomposition can efficiently reduce the
dimensions of dynamical systems but are naturally limited, eg, for convection‐dominated …
dimensions of dynamical systems but are naturally limited, eg, for convection‐dominated …
[HTML][HTML] Energy-conserving hyper-reduction and temporal localization for reduced order models of the incompressible Navier-Stokes equations
RB Klein, B Sanderse - Journal of Computational Physics, 2024 - Elsevier
A novel hyper-reduction method is proposed that conserves kinetic energy and momentum
for reduced order models of the incompressible Navier-Stokes equations. The main …
for reduced order models of the incompressible Navier-Stokes equations. The main …
[HTML][HTML] Adjacency-based, non-intrusive model reduction for Vortex-Induced Vibrations
Vortex-induced Vibrations (VIV) pose computationally expensive problems of high practical
interest to several engineering fields. In this work we develop a non-intrusive, reduced-order …
interest to several engineering fields. In this work we develop a non-intrusive, reduced-order …