Publications related to A matrix DEIM technique for model reduction of nonlinear parametrized problems in cardiac mechanics

Related publications (28)

Spectral Estimators for High-Dimensional Matrix Inference

A key challenge across many disciplines is to extract meaningful information from data which is often obscured by noise. These datasets are typically represented as large matrices. Given the current trend of ever-increasing data volumes, with datasets grow ...

EPFL2024

Stability: a search for structure

Wouter Jongeneel

In this thesis we study stability from several viewpoints. After covering the practical importance, the rich history and the ever-growing list of manifestations of stability, we study the following. (i) (Statistical identification of stable dynamical syste ...

EPFL2024

Implementation of an epicardial implantable MEMS sensor for continuous and real-time postoperative assessment of left ventricular activity in adult minipigs over a short- and long-term period

Silvestro Micera

The sensing of left ventricular (LV) activity is fundamental in the diagnosis and monitoring of cardiovascular health in high-risk patients after cardiac surgery to achieve better short- and long-term outcome. Conventional approaches rely on noninvasive me ...

Aip Publishing2024

Data-Driven Control Synthesis using Koopman Operator: A Robust Approach

Alireza Karimi, Nathan Russell Powell, Mert Eyuboglu

This paper proposes a data-driven control design method for nonlinear systems that builds upon the Koopman operator framework. In particular, the Koopman operator is used to lift the nonlinear dynamics to a higher-dimensional space where the so-called obse ...

2024

Novel theory and potential applications of central diastolic pressure decay time constant

Nikolaos Stergiopoulos, Vasiliki Bikia, Georgios Rovas, Sokratis Anagnostopoulos

Central aortic diastolic pressure decay time constant ( ) is according to the two-element Windkessel model equal to the product of total peripheral resistance (R) times total arterial compliance (C ). As such, it is related to arterial stiffness, which has ...

2024

Statistical Inference for Inverse Problems: From Sparsity-Based Methods to Neural Networks

Pakshal Narendra Bohra

In inverse problems, the task is to reconstruct an unknown signal from its possibly noise-corrupted measurements. Penalized-likelihood-based estimation and Bayesian estimation are two powerful statistical paradigms for the resolution of such problems. They ...

EPFL2024

Unified Harmonic Power-Flow and Stability Analysis of Power Grids with Converter-Interfaced Distributed Energy Resources

Johanna Kristin Maria Becker

Modern power distribution systems are experiencing a large-scale integration of Converter-Interfaced Distributed Energy Resources (CIDERs). Their presence complicates the analysis and mitigation of harmonics, whose creation and propagation may be amplified ...

EPFL2024

A Shape Derivative Approach to Domain Simplification

Alessandra Arrigoni, Annalisa Buffa, Ondine Gabrielle Chanon, Jochen Peter Hinz

The objective of this study is to address the difficulty of simplifying the geometric model in which a differential problem is formulated, also called defeaturing, while simultaneously ensuring that the accuracy of the solution is maintained under control. ...

Elsevier Sci Ltd2023

Analysis-aware defeaturing of complex geometries with Neumann features

Pablo Antolin Sanchez, Ondine Gabrielle Chanon

Local modifications of a computational domain are often performed in order to simplify the meshing process and to reduce computational costs and memory requirements. However, removing geometrical features of a domain often introduces a non-negligible error ...

Wiley2023

ReLU Neural Network Galerkin BEM

Fernando José Henriquez Barraza

We introduce Neural Network (NN for short) approximation architectures for the numerical solution of Boundary Integral Equations (BIEs for short). We exemplify the proposed NN approach for the boundary reduction of the potential problem in two spatial dime ...

SPRINGER/PLENUM PUBLISHERS2023

An equilibrated flux a posteriori error estimator for defeaturing problems

Annalisa Buffa, Rafael Vazquez Hernandez, Ondine Gabrielle Chanon

An a posteriori error estimator based on an equilibrated flux reconstruction is proposed for defeaturing problems in the context of finite element discretizations. Defeaturing consists in the simplification of a geometry by removing features that are consi ...

2023

Timing and spatial selection bias in rapid extreme event attribution

Anthony Christopher Davison, Ophélia Mireille Anna Miralles

Selection bias may arise when data have been chosen in a way that subsequent analysis does not account for. Such bias can arise in climate event attribution studies that are performed rapidly after a devastating "trigger event'', whose occurrence correspon ...

ELSEVIER2023

OASIS: Optimisation-based Activity Scheduling with Integrated Simultaneous choice dimensions

Michel Bierlaire, Timothy Michael Hillel, Janody Pougala

Activity-based models offer the potential of a far deeper understanding of daily mobility behaviour than trip-based models. However, activity-based models used both in research and practice have often relied on applying sequential choice models between sub ...

2023

Universal and adaptive methods for robust stochastic optimization

Ali Kavis

Within the context of contemporary machine learning problems, efficiency of optimization process depends on the properties of the model and the nature of the data available, which poses a significant problem as the complexity of either increases ad infinit ...

EPFL2023

Left cardiac vagotomy rapidly reduces contralateral cardiac vagal electrical activity in anesthetized Gottingen minipigs

Silvestro Micera, Ivo Strauss

Background: The impact of acute unilateral injury on spontaneous electrical activity in both vagus nerves at the heart level is poorly understood. We investigated the immediate neuroelectrical response after right or left cardiac vagal nerve transection (V ...

Elsevier Ireland Ltd2023

Efficient approximation of cardiac mechanics through reduced-order modeling with deep learning-based operator approximation

Alfio Quarteroni, Andrea Manzoni

Reducing the computational time required by high-fidelity, full-order models (FOMs) for the solution of problems in cardiac mechanics is crucial to allow the translation of patient-specific simulations into clinical practice. Indeed, while FOMs, such as th ...

Wiley2023

Design and computational optimization of compliance-matching aortic grafts

Nikolaos Stergiopoulos, Vasiliki Bikia, Georgios Rovas

Introduction: Synthetic vascular grafts have been widely used in clinical practice for aortic replacement surgery. Despite their high rates of surgical success, they remain significantly less compliant than the native aorta, resulting in a phenomenon calle ...

FRONTIERS MEDIA SA2023

Augmented Lagrangian Methods for Provable and Scalable Machine Learning

Mehmet Fatih Sahin

Non-convex constrained optimization problems have become a powerful framework for modeling a wide range of machine learning problems, with applications in k-means clustering, large- scale semidefinite programs (SDPs), and various other tasks. As the perfor ...

EPFL2023

lifex-ep: a robust and efficient software for cardiac electrophysiology simulations

Alfio Quarteroni, Francesco Regazzoni, Stefano Pagani

Background: Simulating the cardiac function requires the numerical solution of multi-physics and multi-scale mathematical models. This underscores the need for streamlined, accurate, and high-performance computational tools. Despite the dedicated endeavors ...

BMC2023

Unbiased Likelihood Estimation of Wright–Fisher Diffusion Processes

Fabio Nobile

In this paper we propose a Monte Carlo maximum likelihood estimation strategy for discretely observed Wright–Fisher diffusions. Our approach provides an unbiased estimator of the likelihood function and is based on exact simulation techniques that are of s ...

Springer Cham2024