153 citations · 456 across the 10 of their papers we have counts for
10 papers
Mechanical Characterization and Inverse Design of Stochastic Architected Metamaterials Using Neural Operators
Hanxun Jin, Enrui Zhang, Boyu Zhang +3
Machine learning (ML) is emerging as a transformative tool for the design of architected materials, offering properties that far surpass those achievable through lab-based trial-an…
A Generative Modeling Framework for Inferring Families of Biomechanical Constitutive Laws in Data-Sparse Regimes
Minglang Yin, Zongren Zou, Enrui Zhang +3
Quantifying biomechanical properties of the human vasculature could deepen our understanding of cardiovascular diseases. Standard nonlinear regression in constitutive modeling requ…
Recent Advances and Applications of Machine Learning in Experimental Solid Mechanics: A Review
Hanxun Jin, Enrui Zhang, Horacio D. Espinosa
For many decades, experimental solid mechanics has played a crucial role in characterizing and understanding the mechanical properties of natural and novel materials. Recent advanc…
On the Geometry Transferability of the Hybrid Iterative Numerical Solver for Differential Equations
Adar Kahana, Enrui Zhang, Somdatta Goswami +3
The discovery of fast numerical solvers prompted a clear and rapid shift towards iterative techniques in many applications, especially in computational mechanics, due to the increa…
G2Φnet: Relating Genotype and Biomechanical Phenotype of Tissues with Deep Learning
Enrui Zhang, Bart Spronck, Jay D. Humphrey +1
Many genetic mutations adversely affect the structure and function of load-bearing soft tissues, with clinical sequelae often responsible for disability or death. Parallel advances…
Blending Neural Operators and Relaxation Methods in PDE Numerical Solvers
Enrui Zhang, Adar Kahana, Alena Kopaničáková +4
Neural networks suffer from spectral bias having difficulty in representing the high frequency components of a function while relaxation methods can resolve high frequencies effici…
Interfacing Finite Elements with Deep Neural Operators for Fast Multiscale Modeling of Mechanics Problems
Minglang Yin, Enrui Zhang, Yue Yu +1
Multiscale modeling is an effective approach for investigating multiphysics systems with largely disparate size features, where models with different resolutions or heterogeneous d…
Simulating progressive intramural damage leading to aortic dissection using an operator-regression neural network
Minglang Yin, Ehsan Ban, Bruno V. Rego +4
Aortic dissection progresses via delamination of the medial layer of the wall. Notwithstanding the complexity of this process, insight has been gleaned by studying in vitro and in…
Strong bulk-surface interaction dominated in-plane anisotropy of electronic structure in GaTe
Kang Lai, Sailong Ju, Hongen Zhu +11
Recently, intriguing physical properties have been unraveled in anisotropic layered semiconductors, in which the in-plane electronic band structure anisotropy often originates from…
Physics-Informed Neural Networks for Nonhomogeneous Material Identification in Elasticity Imaging
Enrui Zhang, Minglang Yin, George Em Karniadakis
We apply Physics-Informed Neural Networks (PINNs) for solving identification problems of nonhomogeneous materials. We focus on the problem with a background in elasticity imaging,…