no code implementations • 23 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.
no code implementations • 4 May 2023 • Minglang Yin, Zongren Zou, Enrui Zhang, Cristina Cavinato, Jay D. Humphrey, George Em Karniadakis
Quantifying biomechanical properties of the human vasculature could deepen our understanding of cardiovascular diseases.
no code implementations • 14 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.
no code implementations • 28 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.
no code implementations • 21 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.
no code implementations • 25 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.
no code implementations • 25 Aug 2021 • Minglang Yin, Ehsan Ban, Bruno V. Rego, Enrui Zhang, Cristina Cavinato, Jay D. Humphrey, George Em Karniadakis
Aortic dissection progresses via delamination of the medial layer of the wall.
no code implementations • 2 Sep 2020 • Enrui Zhang, Minglang Yin, George Em. Karniadakis
We apply Physics-Informed Neural Networks (PINNs) for solving identification problems of nonhomogeneous materials.