The present work proposes a framework for nonlinear model order reduction based on a
Graph Convolutional Autoencoder (GCA-ROM). In the reduced order modeling (ROM) …

Modern computational soft-tissue mechanics models have the potential to offer unique,
patient-specific diagnostic insights. The deployment of such models in clinical settings has …

Mesh-based simulations play a key role when modeling complex physical systems that, in
many disciplines across science and engineering, require the solution to parametrized time …

Abstract We introduce Branched Latent Neural Maps (BLNMs) to learn finite dimensional
input–output maps encoding complex physical processes. A BLNM is defined by a simple …

Computational, or in-silico, models are an effective, non-invasive tool for investigating
cardiovascular function. These models can be used in the analysis of experimental and …

Over the past decades, hemodynamics simulators have steadily evolved and have become
tools of choice for studying cardiovascular systems in-silico. While such tools are routinely …

This work develops a model for natural convection in annuli with internal heat sources
based on Graph Convolution Network (GCN), achieving rapid prediction by directly …

Deep learning advancements have significantly benefited medical applications. One such
helpful application is noninvasive fractional flow reserve (FFR) evaluation along the …

Neural Ordinary Differential Equations (ODEs) represent a significant advancement at the
intersection of machine learning and dynamical systems, offering a continuous-time analog …

In recent years, blending mechanistic knowledge with machine learning has had a major
impact in digital healthcare. In this work, we introduce a computational pipeline to build …