Jan N. Fuhg
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View article: A physics-augmented neural network framework for modeling and detecting thermo-visco-plastic behavior
A physics-augmented neural network framework for modeling and detecting thermo-visco-plastic behavior Open
Although considerable attention has been devoted to the development of models for isothermal, rate-independent plasticity, many high-consequence performance assessments involve viscoplastic processes that generate substantial heat. In addi…
View article: Understanding the Mechanisms Behind the Annuloplasty Effect in Tricuspid Valve TEER: A Computational Study
Understanding the Mechanisms Behind the Annuloplasty Effect in Tricuspid Valve TEER: A Computational Study Open
Background An annuloplasty effect has been observed following tricuspid transcatheter edge-to-edge repair (TEER) and has been shown to have a therapeutic benefit. However, the mechanisms underlying the annuloplasty effect remain unknown. O…
View article: In vitro blood clot mechanical properties depend on fibrinogen and white blood cell subtypes in addition to hematocrit
In vitro blood clot mechanical properties depend on fibrinogen and white blood cell subtypes in addition to hematocrit Open
Background Understanding the role of blood composition in clot mechanics may provide critical cues toward their diagnosis and treatment. To this end, we previously showed that sex and standard blood composition measures explain some variab…
View article: Deep Inverse Rosenblatt Transport for Structural Reliability Analysis
Deep Inverse Rosenblatt Transport for Structural Reliability Analysis Open
Accurately estimating the probability of failure in engineering systems under uncertainty is a fundamental challenge, particularly in high-dimensional settings and for rare events. Conventional reliability analysis methods often become com…
View article: A General, Automated Method for Building Structural Tensors of Arbitrary Order for Anisotropic Function Representations
A General, Automated Method for Building Structural Tensors of Arbitrary Order for Anisotropic Function Representations Open
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 nullspace of a linear operator. The proposed method utilizes sta…
View article: Bubble Dynamics Transformer: Microrheology at Ultra-High Strain Rates
Bubble Dynamics Transformer: Microrheology at Ultra-High Strain Rates Open
Laser-induced inertial cavitation (LIC)-where microscale vapor bubbles nucleate due to a focused high-energy pulsed laser and then violently collapse under surrounding high local pressures-offers a unique opportunity to investigate soft bi…
View article: Polyconvex Physics-Augmented Neural Network Constitutive Models in Principal Stretches
Polyconvex Physics-Augmented Neural Network Constitutive Models in Principal Stretches Open
Accurate constitutive models of soft materials are crucial for understanding their mechanical behavior and ensuring reliable predictions in the design process. To this end, scientific machine learning research has produced flexible and gen…
View article: Input Specific Neural Networks
Input Specific Neural Networks Open
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 architectures that ensure the network's output adheres to a …
View article: An attention-based neural ordinary differential equation framework for modeling inelastic processes
An attention-based neural ordinary differential equation framework for modeling inelastic processes Open
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 state variable-neural ordinary differential equation (ISV-NO…
View article: An Attention-Based Neural Ordinary Differential Equation Framework for Modeling Inelastic Processes
An Attention-Based Neural Ordinary Differential Equation Framework for Modeling Inelastic Processes Open
View article: Inverse design of anisotropic microstructures using physics-augmented neural networks
Inverse design of anisotropic microstructures using physics-augmented neural networks Open
Composite materials often exhibit mechanical anisotropy owing to the material properties or geometrical configurations of the microstructure. This makes their inverse design a two-fold problem. First, we must learn the type and orientation…
View article: Automated model discovery of finite strain elastoplasticity from uniaxial experiments
Automated model discovery of finite strain elastoplasticity from uniaxial experiments Open
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 phenomenological modeling where simple mathematical relationshi…
View article: Polyconvex neural network models of thermoelasticity
Polyconvex neural network models of thermoelasticity Open
View article: Improving the performance of Stein variational inference through extreme sparsification of physically-constrained neural network models
Improving the performance of Stein variational inference through extreme sparsification of physically-constrained neural network models Open
Most scientific machine learning (SciML) applications of neural networks involve hundreds to thousands of parameters, and hence, uncertainty quantification for such models is plagued by the curse of dimensionality. Using physical applicati…
View article: Physics-informed data-driven discovery of constitutive models with application to strain-rate-sensitive soft materials
Physics-informed data-driven discovery of constitutive models with application to strain-rate-sensitive soft materials Open
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 machine learning. This approach is demonstrated on strain-rate-se…
View article: Establishing the relationship between generalized crystallographic texture and macroscopic yield surfaces using partial input convex neural networks
Establishing the relationship between generalized crystallographic texture and macroscopic yield surfaces using partial input convex neural networks Open
View article: A review on data-driven constitutive laws for solids
A review on data-driven constitutive laws for solids Open
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-dependent response of solids. Our objective is to provide an or…
View article: Polyconvex neural network models of thermoelasticity
Polyconvex neural network models of thermoelasticity Open
Machine-learning function representations such as neural networks have proven to be excellent constructs for constitutive modeling due to their flexibility to represent highly nonlinear data and their ability to incorporate constitutive co…
View article: Extreme sparsification of physics-augmented neural networks for interpretable model discovery in mechanics
Extreme sparsification of physics-augmented neural networks for interpretable model discovery in mechanics Open
View article: Extreme sparsification of physics-augmented neural networks for interpretable model discovery in mechanics
Extreme sparsification of physics-augmented neural networks for interpretable model discovery in mechanics Open
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 and to overcome the challenging and time-consuming task of…
View article: Stress representations for tensor basis neural networks: alternative formulations to Finger-Rivlin-Ericksen
Stress representations for tensor basis neural networks: alternative formulations to Finger-Rivlin-Ericksen Open
Data-driven constitutive modeling frameworks based on neural networks and classical representation theorems have recently gained considerable attention due to their ability to easily incorporate constitutive constraints and their excellent…
View article: NN-EVP: A physics informed neural network-based elasto-viscoplastic framework for predictions of grain size-aware flow response under large deformations
NN-EVP: A physics informed neural network-based elasto-viscoplastic framework for predictions of grain size-aware flow response under large deformations Open
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 underlying grain size. The developed NN-EVP algorithm is based …
View article: Deep convolutional Ritz method: parametric PDE surrogates without labeled data
Deep convolutional Ritz method: parametric PDE surrogates without labeled data Open
The parametric surrogate models for partial differential equations (PDEs) are a necessary component for many applications in computational sciences, and the convolutional neural networks (CNNs) have proven to be an excellent tool to genera…
View article: Physics-informed Data-driven Discovery of Constitutive Models with Application to Strain-Rate-sensitive Soft Materials
Physics-informed Data-driven Discovery of Constitutive Models with Application to Strain-Rate-sensitive Soft Materials Open
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 machine learning. This approach is demonstrated on strain-rate-se…
View article: Modular machine learning-based elastoplasticity: Generalization in the context of limited data
Modular machine learning-based elastoplasticity: Generalization in the context of limited data Open
View article: Enhancing phenomenological yield functions with data: Challenges and opportunities
Enhancing phenomenological yield functions with data: Challenges and opportunities Open
International audience
View article: Enhancing high-fidelity nonlinear solver with reduced order model
Enhancing high-fidelity nonlinear solver with reduced order model Open
View article: Modular machine learning-based elastoplasticity: generalization in the context of limited data
Modular machine learning-based elastoplasticity: generalization in the context of limited data Open
The development of accurate constitutive models for materials that undergo path-dependent processes continues to be a complex challenge in computational solid mechanics. Challenges arise both in considering the appropriate model assumption…
View article: Computational Analysis of Coupled Geoscience Processes in Fractured and Deformable Media
Computational Analysis of Coupled Geoscience Processes in Fractured and Deformable Media Open
Prediction of flow, transport, and deformation in fractured and porous media is critical to improving our scientific understanding of coupled thermal-hydrological-mechanical processes related to subsurface energy storage and recovery, nonp…
View article: Learning hyperelastic anisotropy from data via a tensor basis neural network
Learning hyperelastic anisotropy from data via a tensor basis neural network Open