Deep learning-based reduced order models (DL-ROMs) have been recently proposed to
overcome common limitations shared by conventional reduced order models (ROMs)–built …

The rapid growth of deep learning research, including within the field of computational
mechanics, has resulted in an extensive and diverse body of literature. To help researchers …

Eigenvector continuation is a computational method for parametric eigenvalue problems that
uses subspace projection with a basis derived from eigenvector snapshots from different …

When evaluating quantities of interest that depend on the solutions to differential equations,
we inevitably face the trade-off between accuracy and efficiency. Especially for …

Effective data-driven PDE forecasting methods often rely on fixed spatial and/or temporal
discretizations. This raises limitations in real-world applications like weather prediction …

We propose a machine learning-based method to build a system of differential equations
that approximates the dynamics of 3D electromechanical models for the human heart …

In order to simulate the cardiac function for a patient‐specific geometry, the generation of the
computational mesh is crucially important. In practice, the input is typically a set of …

Recent years have witnessed a growth in mathematics for deep learning—which seeks a
deeper understanding of the concepts of deep learning with mathematics and explores how …

Data-driven approaches to modeling physical systems fail to generalize to unseen systems
that share the same general dynamics with the learning domain, but correspond to different …

The numerical simulation of multiple scenarios easily becomes computationally prohibitive
for cardiac electrophysiology (EP) problems if relying on usual high-fidelity, full order models …