191 citations · 694 across the 41 of their papers we have counts for
23 papers · 1 filter
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…
Bayesian Posterior Sampling for Synthetic Shape Generation of Heart Valves
Vijay Dubey, Sumedh Seetharam, Nikos Manthatis +9
Statistical shape models (SSMs) for heart valves commonly rely on principal component analysis (PCA). They are used to support downstream tasks, including \textit{in silico} modeli…
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…
Multiscale Structural Reliability Analysis in high dimensions with Tensor Trains and Physics-Augmented Neural Networks
Aryan Tyagi, Alex de Beer, Tiangang Cui +1
Structural reliability evaluation for composites constitutes a fundamentally high-dimensional multiscale problem, as microscale material uncertainties must propagate to the macrosc…
Fully Differentiable Ultrasound Simulation Utilizing Ray-Tracing
L. River Spencer, Reagan A. Cardoza, Vijay K. Dubey +5
Ultrasound imaging tasks such as calibration, inverse parameter estimation, and acquisition design require models that are physically grounded, efficient, and differentiable with r…
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…
Deep Inverse Rosenblatt Transport for Structural Reliability Analysis
Aryan Tyagi, Jan N. Fuhg
Accurately estimating the probability of failure in engineering systems under uncertainty is a fundamental challenge, particularly in high-dimensional settings and for rare events.…
Thermodynamically Consistent Hybrid and Permutation-Invariant Neural Yield Functions for Anisotropic Plasticity
Asghar A. Jadoon, Ravi G. Patel, Brian N. Granzow +3
Plastic anisotropy in metals remains challenging to model. This is partly because conventional phenomenological yield criteria struggle to combine a highly descriptive, flexible re…
Graph Neural Network Surrogates for Contacting Deformable Bodies with Necessary and Sufficient Contact Detection
Vijay K. Dubey, Collin E. Haese, Osman Gültekin +3
Surrogate models for the rapid inference of nonlinear boundary value problems in mechanics are helpful in a broad range of engineering applications. However, effective surrogate mo…
A comparative study of calibration techniques for finite strain elastoplasticity: Numerically-exact sensitivities for FEMU and VFM
Sanjeev Kumar, D. Thomas Seidl, Brian N. Granzow +2
Accurate identification of material parameters is crucial for predictive modeling in computational mechanics. The two primary approaches in the experimental mechanics' community fo…
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-…
Automated model discovery of finite strain elastoplasticity from uniaxial experiments
Asghar A. Jadoon, Knut A. Meyer, Jan N. Fuhg
Constitutive modeling lies at the core of mechanics, allowing us to map strains onto stresses for a material in a given mechanical setting. Historically, researchers relied on phen…
A review on data-driven constitutive laws for solids
Jan Niklas Fuhg, Govinda Anantha Padmanabha, Nikolaos Bouklas +6
This review article highlights state-of-the-art data-driven techniques to discover, encode, surrogate, or emulate constitutive laws that describe the path-independent and path-depe…
Extreme sparsification of physics-augmented neural networks for interpretable model discovery in mechanics
Jan N. Fuhg, Reese E. Jones, Nikolaos Bouklas
Data-driven constitutive modeling with neural networks has received increased interest in recent years due to its ability to easily incorporate physical and mechanistic constraints…
NN-EVP: A physics informed neural network-based elasto-viscoplastic framework for predictions of grain size-aware flow response under large deformations
Adnan Eghtesad, Jan Niklas Fuhg, Nikolaos Bouklas
We propose a physics informed, neural network-based elasto-viscoplasticity (NN-EVP) constitutive modeling framework for predicting the flow response in metals as a function of unde…
Physics-informed Data-driven Discovery of Constitutive Models with Application to Strain-Rate-sensitive Soft Materials
Kshitiz Upadhyay, Jan N. Fuhg, Nikolaos Bouklas +1
A novel data-driven constitutive modeling approach is proposed, which combines the physics-informed nature of modeling based on continuum thermodynamics with the benefits of machin…
Deep Convolutional Ritz Method: Parametric PDE surrogates without labeled data
Jan Niklas Fuhg, Arnav Karmarkar, Teeratorn Kadeethum +2
Parametric surrogate models for partial differential equations (PDEs) are a necessary component for many applications in the computational sciences, and convolutional neural networ…
On physics-informed data-driven isotropic and anisotropic constitutive models through probabilistic machine learning and space-filling sampling
Jan Niklas Fuhg, Nikolaos Bouklas
Data-driven constitutive modeling is an emerging field in computational solid mechanics with the prospect of significantly relieving the computational costs of hierarchical computa…
Local approximate Gaussian process regression for data-driven constitutive laws: Development and comparison with neural networks
Jan Niklas Fuhg, Michele Marino, Nikolaos Bouklas
Hierarchical computational methods for multiscale mechanics such as the FE and FE-FFT methods are generally accompanied by high computational costs. Data-driven approaches are…
The mixed deep energy method for resolving concentration features in finite strain hyperelasticity
Jan N. Fuhg, Nikolaos Bouklas
The introduction of Physics-informed Neural Networks (PINNs) has led to an increased interest in deep neural networks as universal approximators of PDEs in the solid mechanics comm…
A machine learning based plasticity model using proper orthogonal decomposition
Dengpeng Huang, Jan Niklas Fuhg, Christian Weißenfels +1
Data-driven material models have many advantages over classical numerical approaches, such as the direct utilization of experimental data and the possibility to improve performance…