191 citations · 694 across the 41 of their papers we have counts for
45 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…
Structure-Informed Data-Driven Reduced-Order Modeling of Scalar Hyperbolic Conservation Laws via Kinetic Defect Measure
Marissa Llamas, Jan Fuhg, Hannah Lu
Reduced-order modeling of transport-dominated systems remains challenging because moving fronts and shocks are poorly represented by low-dimensional linear subspaces. We develop a…
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…
A hierarchy of thermodynamics learning frameworks for inelastic constitutive modeling
Reese E. Jones, Jan N. Fuhg
Recent advances in physics-augmented neural networks have enabled thermodynamically consistent data-driven constitutive modeling of complex inelastic materials. Most existing appro…
A physics-augmented neural network framework for modeling and detecting thermo-visco-plastic behavior
Reese E. Jones, Asghar Jadoon, D. Thomas Seidl +1
Although considerable attention has been devoted to the development of models for isothermal, rate-independent plasticity, many high-consequence performance assessments involve vis…
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 General, Automated Method for Building Structural Tensors of Arbitrary Order for Anisotropic Function Representations
Ravi G. Patel, Reese E. Jones, D. Thomas Seidl +2
We present a general, constructive procedure to find the basis for tensors of arbitrary order subject to linear constraints by transforming the problem to that of finding the nulls…
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…
Transformer-Based Inverse Microrheology for Experimental Mechanics at Ultra-High Strain Rates
Lehu Bu, Zhaohan Yu, Danila Frolkin +5
Traditional rheological tools are often limited in characterizing soft materials under ultra-high strain-rate loading conditions (> 1000 s^-1) due to constraints in spatiotemporal…
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…
Input Specific Neural Networks
Asghar A. Jadoon, D. Thomas Seidl, Reese E. Jones +1
The black-box nature of neural networks limits the ability to encode or impose specific structural relationships between inputs and outputs. While various studies have introduced a…
An attention-based neural ordinary differential equation framework for modeling inelastic processes
Reese E. Jones, Jan N. Fuhg
To preserve strictly conservative behavior as well as model the variety of dissipative behavior displayed by solid materials, we propose a significant enhancement to the internal s…
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…
Improving the performance of Stein variational inference through extreme sparsification of physically-constrained neural network models
Govinda Anantha Padmanabha, Jan Niklas Fuhg, Cosmin Safta +2
Most scientific machine learning (SciML) applications of neural networks involve hundreds to thousands of parameters, and hence, uncertainty quantification for such models is plagu…
Multiscale simulation of spatially correlated microstructure via a latent space representation
Reese E. Jones, Craig M. Hamel, Dan Bolintineanu +5
When deformation gradients act on the scale of the microstructure of a part due to geometry and loading, spatial correlations and finite-size effects in simulation cells cannot be…