Publications related to MATHICSE Technical Report : Fast simulations of patient-specific haemodynamics of coronary artery bypass grafts based on a Pod-Galerkin method and a vascular shape parametrization

The role of computational methods in cardiovascular medicine: a narrative review

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Background and Objective: Computational models of the cardiovascular system allow for a detailed and quantitative investigation of both physiological and pathological conditions, thanks to their ability to combine clinical-possibly patient-specific-data wi ...

Ame Publishing Company2024

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

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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 ...

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Devices fabricated from Soft Magnetic Composites (SMCs) are gaining popularity in research and application. The multiscale characteristics require special attention. Solving the quasi-statics Maxwell’s equations on such devices consumes huge time and memor ...

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A reduced basis approach to large-scale pseudospectra computation

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For studying spectral properties of a nonnormal matrix A is an element of Cnxn, information about its spectrum sigma(A) alone is usually not enough. Effects of perturbations on sigma(A) can be studied by computing epsilon-pseudospectra, i.e. the level sets ...

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Computational methods are crucial for the analysis of diffusion magnetic resonance imaging (MRI) of the brain. Computational diffusion MRI can provide rich information at many size scales, including local microstructure measures such as diffusion anisotrop ...

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MATHICSE Technical Report : Fast simulations of patient-specific haemodynamics of coronary artery bypass grafts based on a Pod-Galerkin method and a vascular shape parametrization

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The role of computational methods in cardiovascular medicine: a narrative review

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

Fast in vivo assay of creatine kinase activity in the human brain by P-31 magnetic resonance fingerprinting

LOD-Homogenization of Multiscale Eddy Current Problem in Time Domain

Predicting future myocardial infarction from angiographies with deep learning

Biophysically detailed mathematical models of multiscale cardiac active mechanics

Computational Analysis of Turbulent Hemodynamics in Radiocephalic Arteriovenous Fistulas to Determine the Best Anastomotic Angles

A reduced basis approach to large-scale pseudospectra computation

A finite element framework based on bivariate simplex splines on triangle configurations

Advances in computational and statistical diffusion MRI

The role of computational methods in cardiovascular medicine: a narrative review

LOD-Homogenization of Multiscale Eddy Current Problem in Time Domain

Fast in vivo assay of creatine kinase activity in the human brain by P-31 magnetic resonance fingerprinting

Biophysically detailed mathematical models of multiscale cardiac active mechanics

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

Predicting future myocardial infarction from angiographies with deep learning

Computational Analysis of Turbulent Hemodynamics in Radiocephalic Arteriovenous Fistulas to Determine the Best Anastomotic Angles

Advances in computational and statistical diffusion MRI

A reduced basis approach to large-scale pseudospectra computation

A finite element framework based on bivariate simplex splines on triangle configurations