Recent progress in machine learning (ML), together with advanced computational power,
have provided new research opportunities in cardiovascular modeling. While classifying …

Graph-based next-step prediction models have recently been very successful in modeling
complex high-dimensional physical systems on irregular meshes. However, due to their …

High-dimensional spatio-temporal dynamics can often be encoded in a low-dimensional
subspace. Engineering applications for modeling, characterization, design, and control of …

An adaptive projection-based reduced-order model (ROM) formulation is presented for
model-order reduction of problems featuring chaotic and convection-dominant physics. An …

Solving partial differential equations of complex physical systems is a computationally
expensive task, especially in Computational Fluid Dynamics (CFD). This drives the …

Accurate prediction of dynamical systems in unstructured meshes has recently shown
successes in scientific simulations. Many dynamical systems have a nonnegligible level of …

In this paper we show that our Machine Learning (ML) approach, CoMLSim (Composable
Machine Learning Simulator), can simulate PDEs on highly-resolved grids with higher …

An emerging trend in deep learning research focuses on the applications of graph neural
networks (GNNs) for mesh-based continuum mechanics simulations. Most of these learning …

Thanks to their universal approximation properties and new efficient training strategies,
Deep Neural Networks are becoming a valuable tool for the approximation of mathematical …

The rapid development of deep learning has significant implications for the advancement of
Computational Fluid Dynamics (CFD). Currently, most pixel-grid-based deep learning …