70 citations · 137 across the 10 of their papers we have counts for
10 papers
Monotone Peridynamic Neural Operator for Nonlinear Material Modeling with Conditionally Unique Solutions
Jihong Wang, Xiaochuan Tian, Zhongqiang Zhang +3
Data-driven methods have emerged as powerful tools for modeling the responses of complex nonlinear materials directly from experimental measurements. Among these methods, the data-…
Nonlocal Attention Operator: Materializing Hidden Knowledge Towards Interpretable Physics Discovery
Yue Yu, Ning Liu, Fei Lu +3
Despite the recent popularity of attention-based neural architectures in core AI fields like natural language processing (NLP) and computer vision (CV), their potential in modeling…
Large language models, physics-based modeling, experimental measurements: the trinity of data-scarce learning of polymer properties
Ning Liu, Siavash Jafarzadeh, Brian Y. Lattimer +3
Large language models (LLMs) bear promise as a fast and accurate material modeling paradigm for evaluation, analysis, and design. Their vast number of trainable parameters necessit…
An Ultra-high-speed Reproducing Kernel Particle Method
Siavash Jafarzadeh, Michael Hillman
In this work, the fast-convolving reproducing kernel particle method (FC-RKPM) is introduced. This method is hundreds to millions of times faster than the traditional RKPM for 3D m…
Heterogeneous Peridynamic Neural Operators: Discover Biotissue Constitutive Law and Microstructure From Digital Image Correlation Measurements
Siavash Jafarzadeh, Stewart Silling, Lu Zhang +5
Human tissues are highly organized structures with collagen fiber arrangements varying from point to point. Anisotropy of the tissue arises from the natural orientation of the fibe…
Peridynamic Neural Operators: A Data-Driven Nonlocal Constitutive Model for Complex Material Responses
Siavash Jafarzadeh, Stewart Silling, Ning Liu +2
Neural operators, which can act as implicit solution operators of hidden governing equations, have recently become popular tools for learning the responses of complex real-world ph…
Domain Agnostic Fourier Neural Operators
Ning Liu, Siavash Jafarzadeh, Yue Yu
Fourier neural operators (FNOs) can learn highly nonlinear mappings between function spaces, and have recently become a popular tool for learning responses of complex physical syst…
A general and fast convolution-based method for peridynamics: applications to elasticity and brittle fracture
Siavash Jafarzadeh, Farzaneh Mousavi, Adam Larios +1
We introduce a general and fast convolution-based method (FCBM) for peridynamics (PD). Expressing the PD integrals in terms of convolutions and computing them by fast Fourier trans…
An ordinary state-based peridynamic elastoplastic 2D model consistent with J2 plasticity
Farzaneh Mousavi, Siavash Jafarzadeh, Florin Bobaru
We present a new ordinary state-based peridynamic model (OSB-PD) in 2D consistent with J2 plasticity using a novel decomposition for force and extension states. A new strategy for…
Efficient solutions for nonlocal diffusion problems via boundary-adapted spectral methods
Siavash Jafarzadeh, Adam Larios, Florin Bobaru
We introduce an efficient boundary-adapted spectral method for peridynamic diffusion problems with arbitrary boundary conditions. The spectral approach transforms the convolution i…