117 citations · 126 across the 9 of their papers we have counts for
17 papers
MetaNO: How to Transfer Your Knowledge on Learning Hidden Physics
Lu Zhang, Huaiqian You, Tian Gao +3
Gradient-based meta-learning methods have primarily been applied to classical machine learning tasks such as image classification. Recently, PDE-solving deep learning methods, such…
INO: Invariant Neural Operators for Learning Complex Physical Systems with Momentum Conservation
Ning Liu, Yue Yu, Huaiqian You +1
Neural operators, which emerge as implicit solution operators of hidden governing equations, have recently become popular tools for learning responses of complex real-world physica…
Towards a unified nonlocal, peridynamics framework for the coarse-graining of molecular dynamics data with fractures
Huaiqian You, Xiao Xu, Yue Yu +3
Molecular dynamics (MD) has served as a powerful tool for designing materials with reduced reliance on laboratory testing. However, the use of MD directly to treat the deformation…
OBMeshfree: An optimization-based meshfree solver for nonlocal diffusion and peridynamics models
Yiming Fan, Huaiqian You, Yue Yu
We present OBMeshfree, an Optimization-Based Meshfree solver for compactly supported nonlocal integro-differential equations (IDEs) that can describe material heterogeneity and bri…
MetaNOR: A Meta-Learnt Nonlocal Operator Regression Approach for Metamaterial Modeling
Lu Zhang, Huaiqian You, Yue Yu
We propose MetaNOR, a meta-learnt approach for transfer-learning operators based on the nonlocal operator regression. The overall goal is to efficiently provide surrogate models fo…
A Physics-Guided Neural Operator Learning Approach to Model Biological Tissues from Digital Image Correlation Measurements
Huaiqian You, Quinn Zhang, Colton J. Ross +3
We present a data-driven workflow to biological tissue modeling, which aims to predict the displacement field based on digital image correlation (DIC) measurements under unseen loa…