113 citations · 243 across the 18 of their papers we have counts for
19 papers
Fully differentiable framework for inverse identification of geometry and material parameters with application to determining stress-free configuration of soft tissues
Hyunoh Bae, Tianyi Hu, Adrian Buganza Tepole +1
Medical images of soft tissues typically depict loaded configurations rather than true unloaded (stress-free) states, which can bias biomechanical simulations and inverse material…
Full-Field Calibration of Coupled Thermomechanical Material Models at Finite Strain
L. River Spencer, William D. Meador, Adrian Buganza Tepole +5
Calibrating thermomechanical material models from experiments is challenging because deformation, temperature, and force responses are strongly coupled, while measurements are usua…
Stable Long-Horizon Neural ODE Reduced-Order Models via Learned Feedback for Biological Growth and Remodeling
Joel Laudo, Adrian Buganza Tepole
Reduced-order models (ROMs) are essential for rapid simulation of complex biomechanical systems and for bridging the gap between high fidelity models and clinical application. Howe…
A Differentiable Framework for Gradient Enhanced Damage with Physics-Augmented Neural Networks in JAX-FEM
Mark Wilkinson, Amirhossein Amiri-Hezaveh, Adrian Buganza Tepole
Soft materials such as rubbers, hydrogels, and biological tissues undergo damage in the form of stiffness degradation without apparent changes in their stress-free geometry. Accura…
The phase-field model of fracture incorporating Mohr-Coulomb, Mogi-Coulomb, and Hoek-Brown strength surfaces
S Chockalingam, Adrian Buganza Tepole, Aditya Kumar
Classical phase-field theories of brittle fracture capture toughness-controlled crack growth but do not account for the material's strength surface, which governs fracture nucleati…
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…
A Physics-Augmented Machine Learning Constitutive Model for Damage in Solids
Amirhossein Amiri-Hezaveh, Adrian Buganza Tepole
We propose a data-driven constitutive framework for anisotropic damage mechanics based on the second-order damage tensor approach for both compressible and incompressible materials…
Differentiable neural network representation of multi-well, locally-convex potentials
Reese E. Jones, Adrian Buganza Tepole, Jan N. Fuhg
Multi-well potentials are ubiquitous in science, modeling phenomena such as phase transitions, dynamic instabilities, and multimodal behavior across physics, chemistry, and biology…
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…
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…
Benchmarks for physics-informed data-driven hyperelasticity
Vahidullah Tac, Kevin Linka, Francisco Sahli-Costabal +2
Data-driven methods have changed the way we understand and model materials. However, while providing unmatched flexibility, these methods have limitations such as reduced capacity…
Data-driven anisotropic finite viscoelasticity using neural ordinary differential equations
Vahidullah Tac, Manuel K. Rausch, Francisco Sahli-Costabal +1
We develop a fully data-driven model of anisotropic finite viscoelasticity using neural ordinary differential equations as building blocks. We replace the Helmholtz free energy fun…
Elasticity Solver in Minecraft for Learning Mechanics of Materials by Gaming
Zachariah P. Beck, Brandon Alpert, Alexander J. Bowman +2
Video games have emerged as a medium for learning by creating engaging environments, encouraging creative and deep thinking, and exposing learners to complex problems. Unfortunatel…
PDE-constrained shape registration to characterize biological growth and morphogenesis from imaging data
Aishwarya Pawar, Linlin Li, Arun K Gosain +2
We propose a PDE-constrained shape registration algorithm that captures the deformation and growth of biological tissue from imaging data. Shape registration is the process of eval…
Data-driven Tissue Mechanics with Polyconvex Neural Ordinary Differential Equations
Vahidullah Tac, Francisco S. Costabal, Adrian Buganza Tepole
Data-driven methods are becoming an essential part of computational mechanics due to their unique advantages over traditional material modeling. Deep neural networks are able to le…
Data-driven Modeling of the Mechanical Behavior of Anisotropic Soft Biological Tissue
Vahidullah Tac, Vivek D. Sree, Manuel K. Rausch +1
Constitutive models that describe the mechanical behavior of soft tissues have advanced greatly over the past few decades. These expert models are generalizable and require the cal…
Predicting the Mechanical Properties of Biopolymer Gels Using Neural Networks Trained on Discrete Fiber Network Data
Yue Leng, Vahidullah Tac, Sarah Calve +1
Biopolymer gels, such as those made out of fibrin or collagen, are widely used in tissue engineering applications and biomedical research. Moreover, fibrin naturally assembles into…
Improving Reconstructive Surgery Design using Gaussian Process Surrogates to Capture Material Behavior Uncertainty
Casey Stowers, Taeksang Lee, Ilias Bilionis +2
Excessive loads near wounds produce pathological scarring and other complications. Presently, stress cannot easily be measured by surgeons in the operating room. Instead, surgeons…