105 citations · 152 across the 16 of their papers we have counts for
21 papers
Assessing the performance of correlation-based multi-fidelity neural emulators
Cristian J. Villatoro, Gianluca Geraci, Daniele E. Schiavazzi
Outer loop tasks such as optimization, uncertainty quantification or inference can easily become intractable when the underlying high-fidelity model is computationally expensive. S…
On the performance of multi-fidelity and reduced-dimensional neural emulators for inference of physiological boundary conditions
Chloe H. Choi, Andrea Zanoni, Daniele E. Schiavazzi +1
Solving inverse problems in cardiovascular modeling is particularly challenging due to the high computational cost of running high-fidelity simulations. In this work, we focus on B…
Enabling stratified sampling in high dimensions via nonlinear dimensionality reduction
Gianluca Geraci, Daniele E. Schiavazzi, Andrea Zanoni
We consider the problem of propagating the uncertainty from a possibly large number of random inputs through a computationally expensive model. Stratified sampling is a well-known…
Rule-based and reinforcement-learning policies for resource allocation in a pediatric echo lab: a simulation study
Bozhi Sun, Seda Tierney, Jeffrey A. Feinstein +3
Scheduling echocardiographic examinations in a hospital setting presents significant challenges due to non-deterministic factors such as patient no-shows, variable arrival times, a…
Personalized and uncertainty-aware coronary hemodynamics simulations: From Bayesian estimation to improved multi-fidelity uncertainty quantification
Karthik Menon, Andrea Zanoni, Owais Khan +4
Simulations of coronary hemodynamics have improved non-invasive clinical risk stratification and treatment outcomes for coronary artery disease, compared to relying on anatomical i…
InVAErt networks for amortized inference and identifiability analysis of lumped parameter hemodynamic models
Guoxiang Grayson Tong, Carlos A. Sing Long, Daniele E. Schiavazzi
Estimation of cardiovascular model parameters from electronic health records (EHR) poses a significant challenge primarily due to lack of identifiability. Structural non-identifiab…
Quantification of total uncertainty in the physics-informed reconstruction of CVSim-6 physiology
Mario De Florio, Zongren Zou, Daniele E. Schiavazzi +1
When predicting physical phenomena through simulation, quantification of the total uncertainty due to multiple sources is as crucial as making sure the underlying numerical model i…
Neural active manifolds: nonlinear dimensionality reduction for uncertainty quantification
Andrea Zanoni, Gianluca Geraci, Matteo Salvador +2
We present a new approach for nonlinear dimensionality reduction, specifically designed for computationally expensive mathematical models. We leverage autoencoders to discover a on…
Bayesian Windkessel calibration using optimized 0D surrogate models
Jakob Richter, Jonas Nitzler, Luca Pegolotti +6
Boundary condition (BC) calibration to assimilate clinical measurements is an essential step in any subject-specific simulation of cardiovascular fluid dynamics. Bayesian calibrati…
Global sensitivity analysis with multifidelity Monte Carlo and polynomial chaos expansion for carotid artery haemodynamics
Friederike Schäfer, Daniele E. Schiavazzi, Leif Rune Hellevik +1
Computational models of the cardiovascular system are increasingly used for the diagnosis, treatment, and prevention of cardiovascular disease. Before being used for translational…
Improved multifidelity Monte Carlo estimators based on normalizing flows and dimensionality reduction techniques
Andrea Zanoni, Gianluca Geraci, Matteo Salvador +3
We study the problem of multifidelity uncertainty propagation for computationally expensive models. In particular, we consider the general setting where the high-fidelity and low-f…
A Probabilistic Neural Twin for Treatment Planning in Peripheral Pulmonary Artery Stenosis
John D. Lee, Jakob Richter, Martin R. Pfaller +8
The substantial computational cost of high-fidelity models in numerical hemodynamics has, so far, relegated their use mainly to offline treatment planning. New breakthroughs in dat…
InVAErt networks: a data-driven framework for model synthesis and identifiability analysis
Guoxiang Grayson Tong, Carlos A. Sing Long, Daniele E. Schiavazzi
Use of generative models and deep learning for physics-based systems is currently dominated by the task of emulation. However, the remarkable flexibility offered by data-driven arc…
Data-driven synchronization-avoiding algorithms in the explicit distributed structural analysis of soft tissue
Guoxiang Grayson Tong, Daniele E. Schiavazzi
We propose a data-driven framework to increase the computational efficiency of the explicit finite element method in the structural analysis of soft tissue. An encoder-decoder long…
Multifidelity data fusion in convolutional encoder/decoder networks
Lauren Partin, Gianluca Geraci, Ahmad Rushdi +2
We analyze the regression accuracy of convolutional neural networks assembled from encoders, decoders and skip connections and trained with multifidelity data. Besides requiring si…
An analysis of reconstruction noise from undersampled 4D flow MRI
Lauren Partin, Daniele E. Schiavazzi, Carlos A. Sing Long
Novel Magnetic Resonance (MR) imaging modalities can quantify hemodynamics but require long acquisition times, precluding its widespread use for early diagnosis of cardiovascular d…
Geometric Uncertainty in Patient-Specific Cardiovascular Modeling with Convolutional Dropout Networks
Gabriel Maher, Casey Fleeter, Daniele Schiavazzi +1
We propose a novel approach to generate samples from the conditional distribution of patient-specific cardiovascular models given a clinically aquired image volume. A convolutional…
Multi-fidelity estimators for coronary circulation models under clinically-informed data uncertainty
Jongmin Seo, Casey Fleeter, Andrew M. Kahn +2
Numerical models are increasingly used for non-invasive diagnosis and treatment planning in coronary artery disease, where service-based technologies have proven successful in iden…
The effects of clinically-derived parametric data uncertainty in patient-specific coronary simulations with deformable walls
Jongmin Seo, Daniele E. Schiavazzi, Andrew M. Kahn +1
Cardiovascular simulations are increasingly used for non-invasive diagnosis of cardiovascular disease, to guide treatment decisions, and in the design of medical devices. Quantitat…
Multilevel and multifidelity uncertainty quantification for cardiovascular hemodynamics
Casey M. Fleeter, Gianluca Geraci, Daniele E. Schiavazzi +2
Standard approaches for uncertainty quantification in cardiovascular modeling pose challenges due to the large number of uncertain inputs and the significant computational cost of…
Performance of preconditioned iterative linear solvers for cardiovascular simulations in rigid and deformable vessels
Jongmin Seo, Daniele E. Schiavazzi, Alison L. Marsden
Computing the solution of linear systems of equations is invariably the most time consuming task in the numerical solutions of PDEs in many fields of computational science. In this…