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

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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 ...

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 ...

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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 ...

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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 ...

We study the problem of drift estimation for two-scale continuous time series. We set ourselves in the framework of overdamped Langevin equations, for which a single-scale surrogate homogenized equation exists. In this setting, estimating the drift coeffic ...

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 ...

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 ...

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 ...

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

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

Unbiased Likelihood Estimation of Wright–Fisher Diffusion Processes

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

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

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

A Shape Derivative Approach to Domain Simplification

Analysis-aware defeaturing of complex geometries with Neumann features

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An equilibrated flux a posteriori error estimator for defeaturing problems

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

Drift Estimation of Multiscale Diffusions Based on Filtered Data

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

Design and computational optimization of compliance-matching aortic grafts

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