Fluid–structure interaction is ubiquitous in nature and occurs at all biological scales.
Immersed methods provide mathematical and computational frameworks for modeling fluid …

In the present work, two machine learning based constitutive models for finite deformations
are proposed. Using input convex neural networks, the models are hyperelastic, anisotropic …

Variationally consistent phase-field methods have been shown to be able to predict complex
three-dimensional crack patterns. However, current computational methodologies in the …

We present a framework for the multiscale modeling of finite strain magneto-elasticity based
on physics-augmented neural networks (NNs). By using a set of problem specific invariants …

Data-driven methods are becoming an essential part of computational mechanics due to
their advantages over traditional material modeling. Deep neural networks are able to learn …

Designing engineering components that make optimal use of materials requires
consideration of the nonlinear static and dynamic characteristics associated with both …

Closed-form constitutive models are currently the standard approach for describing soft
tissues' mechanical behavior. However, there are inherent pitfalls to this approach. For …

Computer modeling and simulation is a powerful tool for assessing the performance of
medical devices such as bioprosthetic heart valves (BHVs) that promises to accelerate …

We propose a new approach for the stabilization of linear tetrahedral finite elements in the
case of nearly incompressible transient solid dynamics computations. Our method is based …

This paper introduces a novel Smooth Particle Hydrodynamics (SPH) computational
framework that incorporates an Arbitrary Lagrangian Eulerian (ALE) formalism, expressed …