Neural-network-augmented projection-based model order reduction for mitigating the Kolmogorov barrier to reducibility
Inspired by our previous work on a quadratic approximation manifold [1], we propose in this
paper a computationally tractable approach for combining a projection-based reduced-order …
paper a computationally tractable approach for combining a projection-based reduced-order …
SVD perspectives for augmenting DeepONet flexibility and interpretability
Deep operator networks (DeepONets) are powerful and flexible architectures that are
attracting attention in multiple fields due to their utility for fast and accurate emulation of …
attracting attention in multiple fields due to their utility for fast and accurate emulation of …
Physics-data combined machine learning for parametric reduced-order modelling of nonlinear dynamical systems in small-data regimes
Repeatedly solving nonlinear partial differential equations with varying parameters is often
an essential requirement to characterise the parametric dependences of dynamical systems …
an essential requirement to characterise the parametric dependences of dynamical systems …
Non-linear manifold reduced-order models with convolutional autoencoders and reduced over-collocation method
Non-affine parametric dependencies, nonlinearities and advection-dominated regimes of
the model of interest can result in a slow Kolmogorov n-width decay, which precludes the …
the model of interest can result in a slow Kolmogorov n-width decay, which precludes the …
Symplectic model reduction of Hamiltonian systems using data-driven quadratic manifolds
This work presents two novel approaches for the symplectic model reduction of high-
dimensional Hamiltonian systems using data-driven quadratic manifolds. Classical …
dimensional Hamiltonian systems using data-driven quadratic manifolds. Classical …
[图书][B] Advanced reduced order methods and applications in computational fluid dynamics
Reduced order modeling is an important and fast-growing research field in computational
science and engineering, motivated by several reasons, of which we mention just a few …
science and engineering, motivated by several reasons, of which we mention just a few …
A DeepONet multi-fidelity approach for residual learning in reduced order modeling
In the present work, we introduce a novel approach to enhance the precision of reduced
order models by exploiting a multi-fidelity perspective and DeepONets. Reduced models …
order models by exploiting a multi-fidelity perspective and DeepONets. Reduced models …
Data-driven reduced order modelling for patient-specific hemodynamics of coronary artery bypass grafts with physical and geometrical parameters
In this work the development of a machine learning-based Reduced Order Model (ROM) for
the investigation of hemodynamics in a patient-specific configuration of Coronary Artery …
the investigation of hemodynamics in a patient-specific configuration of Coronary Artery …
Neural-network learning of SPOD latent dynamics
We aim to reconstruct the latent space dynamics of high dimensional, quasi-stationary
systems using model order reduction via the spectral proper orthogonal decomposition …
systems using model order reduction via the spectral proper orthogonal decomposition …
An optimisation–based domain–decomposition reduced order model for parameter–dependent non–stationary fluid dynamics problems
In this work, we address parametric non–stationary fluid dynamics problems within a model
order reduction setting based on domain decomposition. Starting from the optimisation …
order reduction setting based on domain decomposition. Starting from the optimisation …