117 citations · 394 across the 16 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…
Learning Deep Implicit Fourier Neural Operators (IFNOs) with Applications to Heterogeneous Material Modeling
Huaiqian You, Quinn Zhang, Colton J. Ross +2
Constitutive modeling based on continuum mechanics theory has been a classical approach for modeling the mechanical responses of materials. However, when constitutive laws are unkn…
A Meshfree Peridynamic Model for Brittle Fracture in Randomly Heterogeneous Materials
Yiming Fan, Huaiqian You, Xiaochuan Tian +4
In this work we aim to develop a unified mathematical framework and a reliable computational approach to model the brittle fracture in heterogeneous materials with variability in m…
Nonlocal Kernel Network (NKN): a Stable and Resolution-Independent Deep Neural Network
Huaiqian You, Yue Yu, Marta D'Elia +2
Neural operators have recently become popular tools for designing solution maps between function spaces in the form of neural networks. Differently from classical scientific machin…
Peridynamic Model for Single-Layer Graphene Obtained from Coarse Grained Bond Forces
Stewart A. Silling, Marta D'Elia, Yue Yu +2
An ordinary state-based peridynamic material model is proposed for single sheet graphene. The model is calibrated using coarse grained molecular dynamics simulations. The coarse gr…
A data-driven peridynamic continuum model for upscaling molecular dynamics
Huaiqian You, Yue Yu, Stewart Silling +1
Nonlocal models, including peridynamics, often use integral operators that embed lengthscales in their definition. However, the integrands in these operators are difficult to defin…
An asymptotically compatible treatment of traction loading in linearly elastic peridynamic fracture
Yue Yu, Huaiqian You, Nathaniel Trask
Meshfree discretizations of state-based peridynamic models are attractive due to their ability to naturally describe fracture of general materials. However, two factors conspire to…
Data-driven learning of nonlocal models: from high-fidelity simulations to constitutive laws
Huaiqian You, Yue Yu, Stewart Silling +1
We show that machine learning can improve the accuracy of simulations of stress waves in one-dimensional composite materials. We propose a data-driven technique to learn nonlocal c…
Data-driven learning of robust nonlocal physics from high-fidelity synthetic data
Huaiqian You, Yue Yu, Nathaniel Trask +2
A key challenge to nonlocal models is the analytical complexity of deriving them from first principles, and frequently their use is justified a posteriori. In this work we extract…
An Asymptotically Compatible Formulation for Local-to-Nonlocal Coupling Problems without Overlapping Regions
Huaiqian You, Yue Yu, David Kamensky
In this paper we design and analyze an explicit partitioned procedure for a 2D dynamic local-to-nonlocal (LtN) coupling problem, based on a new nonlocal Robin-type transmission con…
An Asymptotically Compatible Approach For Neumann-Type Boundary Condition On Nonlocal Problems
Huaiqian You, Xin Yang Lu, Nathaniel Trask +1
In this paper we consider 2D nonlocal diffusion models with a finite nonlocal horizon parameter characterizing the range of nonlocal interactions, and consider the treatment of…
An asymptotically compatible meshfree quadrature rule for non-local problems with applications to peridynamics
Nathaniel Trask, Huaiqian You, Yue Yu +1
We present a meshfree quadrature rule for compactly supported non-local integro-differential equations (IDEs) with radial kernels. We apply this rule to develop a strong-form meshf…