no code implementations • 28 Jan 2023 • Lu Zhang, Huaiqian You, Tian Gao, Mo Yu, Chung-Hao Lee, Yue Yu
Gradient-based meta-learning methods have primarily been applied to classical machine learning tasks such as image classification.
no code implementations • 11 Jan 2023 • Huaiqian You, Xiao Xu, Yue Yu, Stewart Silling, Marta D'Elia, John Foster
Then, based on the coarse-grained MD data, a two-phase optimization-based learning approach is proposed to infer the optimal peridynamics model with damage criterion.
no code implementations • 29 Dec 2022 • Ning Liu, Yue Yu, Huaiqian You, Neeraj Tatikola
Neural operators, which emerge as implicit solution operators of hidden governing equations, have recently become popular tools for learning responses of complex real-world physical systems.
no code implementations • 4 Jun 2022 • Lu Zhang, Huaiqian You, Yue Yu
We propose MetaNOR, a meta-learnt approach for transfer-learning operators based on the nonlocal operator regression.
1 code implementation • 1 Apr 2022 • Huaiqian You, Quinn Zhang, Colton J. Ross, Chung-Hao Lee, Ming-Chen Hsu, Yue Yu
To improve the generalizability of our framework, we propose a physics-guided neural operator learning model via imposing partial physics knowledge.
1 code implementation • 15 Mar 2022 • Huaiqian You, Quinn Zhang, Colton J. Ross, Chung-Hao Lee, Yue Yu
In this work, we propose to use data-driven modeling, which directly utilizes high-fidelity simulation and/or experimental measurements to predict a material's response without using conventional constitutive models.
no code implementations • 6 Jan 2022 • Huaiqian You, Yue Yu, Marta D'Elia, Tian Gao, Stewart Silling
In this work, we propose a novel nonlocal neural operator, which we refer to as nonlocal kernel network (NKN), that is resolution independent, characterized by deep neural networks, and capable of handling a variety of tasks such as learning governing equations and classifying images.
no code implementations • 4 Aug 2021 • Huaiqian You, Yue Yu, Stewart Silling, Marta D'Elia
Nonlocal models, including peridynamics, often use integral operators that embed lengthscales in their definition.
no code implementations • 5 Jan 2021 • Yue Yu, Huaiqian You, Nathaniel Trask
In the absence of fracture, when a corresponding classical continuum mechanics model exists, our improvements provide asymptotically compatible convergence to corresponding local solutions, eliminating surface effects and issues with traction loading which have historically plagued peridynamic discretizations.
Numerical Analysis Computational Engineering, Finance, and Science Numerical Analysis Analysis of PDEs
no code implementations • 8 Dec 2020 • Huaiqian You, Yue Yu, Stewart Silling, Marta D'Elia
We show that machine learning can improve the accuracy of simulations of stress waves in one-dimensional composite materials.
no code implementations • 17 May 2020 • Huaiqian You, Yue Yu, Nathaniel Trask, Mamikon Gulian, Marta D'Elia
A key challenge to nonlocal models is the analytical complexity of deriving them from first principles, and frequently their use is justified a posteriori.