18 citations · 20 across the 8 of their papers we have counts for
9 papers
Mixed-Dimensional Electromechanical Coupling of Embedded Beam Networks in Soft Dielectric Composites
L. River Spencer, Manuel K. Rausch, Chad Landis +1
Soft electroactive composites can exhibit strongly architecture-dependent mechanical and electrical responses, but explicitly resolving dense fiber networks in three dimensions is…
Data-Driven Discovery of Composition-Dependent Constitutive Models for Hyperelasticity and Viscoelasticity of Digital Materials
Josué García-Ávila, Beijun Shen, Manuel K. Rausch +2
Digital materials fabricated by multi-material 3D printing are designed as controlled mixtures of stiff and compliant constituents, yielding effective responses that span more than…
Multiscale topology optimization of compressible and nearly incompressible anisotropic hyperelastic structures using physics-augmented neural networks
Asghar A. Jadoon, Aryan Tyagi, L. River Spencer +5
Multiscale topology optimization (TO) of hyperelastic materials remains computationally prohibitive due to the repeated solution of microscale boundary value problems. In this work…
A Monolithic Computational Homogenization Framework for Nearly Incompressible Magnetoelastic Composites
L. River Spencer, Manuel K. Rausch, Chad M. Landis +1
Magneto-active elastomers exhibit large, nonlinear deformations under combined mechanical loading and magnetic fields, and their effective behavior is strongly governed by microstr…
Fully data-driven inverse hyperelasticity with hyper-network neural ODE fields
Vahidullah Taç, Amirhossein Amiri-Hezaveh, Manuel K. Rausch +3
We propose a new framework for identifying mechanical properties of heterogeneous materials without a closed-form constitutive equation. Given a full-field measurement of the displ…
Polyconvex Physics-Augmented Neural Network Constitutive Models in Principal Stretches
Adrian Buganza Tepole, Asghar Jadoon, Manuel Rausch +1
Accurate constitutive models of soft materials are crucial for understanding their mechanical behavior and ensuring reliable predictions in the design process. To this end, scienti…
Inverse design of anisotropic microstructures using physics-augmented neural networks
Asghar A. Jadoon, Karl A. Kalina, Manuel K. Rausch +2
Composite materials often exhibit mechanical anisotropy owing to the material properties or geometrical configurations of the microstructure. This makes their inverse design a two-…
A universal material model subroutine for soft matter systems
Mathias Peirlinck, Juan A. Hurtado, Manuel K. Rausch +2
Soft materials play an integral part in many aspects of modern life including autonomy, sustainability, and human health, and their accurate modeling is critical to understand thei…
Generative Hyperelasticity with Physics-Informed Probabilistic Diffusion Fields
Vahidullah Tac, Manuel K Rausch, Ilias Bilionis +2
Many natural materials exhibit highly complex, nonlinear, anisotropic, and heterogeneous mechanical properties. Recently, it has been demonstrated that data-driven strain energy fu…