Publications related to Structure-preserving approaches and data-driven closure modeling for model order reduction

Structure-Preserving Reduced Basis Methods for Hamiltonian Systems with a Nonlinear Poisson Structure

Jan Sickmann Hesthaven, Cecilia Pagliantini

We develop structure-preserving reduced basis methods for a large class of problems by resorting to their semi-discrete formulation as Hamiltonian dynamical systems. In this perspective, the phase space is naturally endowed with a Poisson manifold structur ...

2018

Structure-Preserving Reduced Basis Methods for Hamiltonian Systems with a State-dependent Poisson Structure

Jan Sickmann Hesthaven, Cecilia Pagliantini

We develop structure-preserving reduced basis methods for a large class of nondissipative problems by resorting to their formulation as Hamiltonian dynamical systems. With this perspective, the phase space is naturally endowed with a Poisson manifold struc ...

2018

Symplectic Model-Reduction with a Weighted Inner Product

Jan Sickmann Hesthaven, Babak Maboudi Afkham

In the recent years, considerable attention has been paid to preserving structures and invariants in reduced basis methods, in order to enhance the stability and robustness of the reduced system. In the context of Hamiltonian systems, symplectic model redu ...

2018

Dynamical Low Rank approximation of PDEs with random parameters

Eleonora Musharbash

In this work, we focus on the Dynamical Low Rank (DLR) approximation of PDEs equations with random parameters. This can be interpreted as a reduced basis method, where the approximate solution is expanded in separable form over a set of few deterministic b ...

EPFL2017

Generating Steganographic Text with LSTMs

Martin Jaggi, Tina Fang

Motivated by concerns for user privacy, we design a steganographic system ("stegosystem") that enables two users to exchange encrypted messages without an adversary detecting that such an exchange is taking place. We propose a new linguistic stegosystem ba ...

2017

Statistical physics approaches to subnetwork dynamics in biochemical systems

Barbara Bravi

We apply a Gaussian variational approximation to model reduction in large biochemical networks of unary and binary reactions. We focus on a small subset of variables (subnetwork) of interest, e.g. because they are accessible experimentally, embedded in a l ...

Iop Publishing Ltd2017

MATHICSE Technical Report : Symplectic dynamical low rank approximation of wave equations with random parameters

Fabio Nobile, Eleonora Musharbash, Eva Vidlicková

In this paper we propose a dynamical low-rank strategy for the approximation of second order wave equations with random parameters. The governing equation is rewritten in Hamiltonian form and the approximate solution is expanded over a set of 2S dynamical ...

MATHICSE2017

Symplectic Dynamical Low Rank approximation of wave equations with random parameters

Fabio Nobile, Eleonora Musharbash

In this paper we propose a dynamical low-rank strategy for the approximation of second order wave equations with random parameters. The governing equation is rewritten in Hamiltonian form and the approximate solution is expanded over a set of

2S

dynamica ...

2017

Structure preserving model reduction of parametric Hamiltonian systems

Jan Sickmann Hesthaven, Babak Maboudi Afkham

While reduced-order models (ROMs) are popular for approximately solving large systems of differential equations, the stability of reduced models over long-time integration remains an open question. We present a greedy approach for ROM generation of paramet ...

2017

Memory performance-related dynamic brain connectivity indicates pathological burden and genetic risk for Alzheimer's disease

Dimitri Nestor Alice Van De Ville, Maria Giulia Preti

Background: The incidence of Alzheimer's disease (AD) strongly relates to advanced age and progressive deposition of cerebral amyloid-beta (A beta), hyperphosphorylated tau, and iron. The purpose of this study was to investigate the relationship between ce ...

Biomed Central Ltd2017

Structure-preserving approaches and data-driven closure modeling for model order reduction

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Structure-Preserving Reduced Basis Methods for Hamiltonian Systems with a Nonlinear Poisson Structure

Structure-Preserving Reduced Basis Methods for Hamiltonian Systems with a State-dependent Poisson Structure

Symplectic Model-Reduction with a Weighted Inner Product

Dynamical Low Rank approximation of PDEs with random parameters

Generating Steganographic Text with LSTMs

Statistical physics approaches to subnetwork dynamics in biochemical systems

MATHICSE Technical Report : Symplectic dynamical low rank approximation of wave equations with random parameters

Symplectic Dynamical Low Rank approximation of wave equations with random parameters

Structure preserving model reduction of parametric Hamiltonian systems

Memory performance-related dynamic brain connectivity indicates pathological burden and genetic risk for Alzheimer's disease

Dynamical Low Rank approximation of PDEs with random parameters

Structure preserving model reduction of parametric Hamiltonian systems

Memory performance-related dynamic brain connectivity indicates pathological burden and genetic risk for Alzheimer's disease

MATHICSE Technical Report : Symplectic dynamical low rank approximation of wave equations with random parameters

Statistical physics approaches to subnetwork dynamics in biochemical systems

Generating Steganographic Text with LSTMs

Symplectic Dynamical Low Rank approximation of wave equations with random parameters

Structure-Preserving Reduced Basis Methods for Hamiltonian Systems with a Nonlinear Poisson Structure

Structure-Preserving Reduced Basis Methods for Hamiltonian Systems with a State-dependent Poisson Structure

Symplectic Model-Reduction with a Weighted Inner Product