Search Results for author: Enrui Zhang

Found 8 papers, 0 papers with code

Mechanical Characterization and Inverse Design of Stochastic Architected Metamaterials Using Neural Operators

no code implementations23 Nov 2023 Hanxun Jin, Enrui Zhang, Boyu Zhang, Sridhar Krishnaswamy, George Em Karniadakis, Horacio D. Espinosa

Our work marks a significant advancement in the field of materials-by-design, potentially heralding a new era in the discovery and development of next-generation metamaterials with unparalleled mechanical characteristics derived directly from experimental insights.

Recent Advances and Applications of Machine Learning in Experimental Solid Mechanics: A Review

no code implementations14 Mar 2023 Hanxun Jin, Enrui Zhang, Horacio D. Espinosa

As the number of papers published in recent years in this emerging field is exploding, it is timely to conduct a comprehensive and up-to-date review of recent ML applications in experimental solid mechanics.

Experimental Design

A Hybrid Iterative Numerical Transferable Solver (HINTS) for PDEs Based on Deep Operator Network and Relaxation Methods

no code implementations28 Aug 2022 Enrui Zhang, Adar Kahana, Eli Turkel, Rishikesh Ranade, Jay Pathak, George Em Karniadakis

Based on recent advances in scientific deep learning for operator regression, we propose HINTS, a hybrid, iterative, numerical, and transferable solver for differential equations.

G2Φnet: Relating Genotype and Biomechanical Phenotype of Tissues with Deep Learning

no code implementations21 Aug 2022 Enrui Zhang, Bart Spronck, Jay D. Humphrey, George Em Karniadakis

Many genetic mutations adversely affect the structure and function of load-bearing soft tissues, with clinical sequelae often responsible for disability or death.

Interfacing Finite Elements with Deep Neural Operators for Fast Multiscale Modeling of Mechanics Problems

no code implementations25 Feb 2022 Minglang Yin, Enrui Zhang, Yue Yu, George Em Karniadakis

In this work, we explore the idea of multiscale modeling with machine learning and employ DeepONet, a neural operator, as an efficient surrogate of the expensive solver.

Physics-Informed Neural Networks for Nonhomogeneous Material Identification in Elasticity Imaging

no code implementations2 Sep 2020 Enrui Zhang, Minglang Yin, George Em. Karniadakis

We apply Physics-Informed Neural Networks (PINNs) for solving identification problems of nonhomogeneous materials.

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