2 citations · 2 across the 6 of their papers we have counts for
6 papers
SPLIT-PINN: Separable Probability Learning Technique via Physics-Informed Neural Networks for High-Dimensional Probabilistic Modeling
Pouria Behnoudfar, Deekshith Naidu Ponnana, Noah J. Schmelzer +6
We present a probabilistic modeling framework for incorporating small-scale spatial heterogeneity into macroscopic descriptions of material behavior for polycrystalline metallic ma…
Towards Quantum Accelerated Large-scale Topology Optimization
Zisheng Ye, Wenxiao Pan
We present a new method that efficiently solves TO problems and provides a practical pathway to leverage quantum computing to exploit potential quantum advantages. This work target…
Error Estimates for the Arnoldi Approximation of a Matrix Square Root
James H. Adler, Xiaozhe Hu, Wenxiao Pan +1
The Arnoldi process provides an efficient framework for approximating functions of a matrix applied to a vector, i.e., of the form , by repeated matrix-vector multiplic…
-HIGNN: A Scalable Graph Neural Network Framework with Hierarchical Matrix Acceleration for Simulation of Large-Scale Particulate Suspensions
Zhan Ma, Zisheng Ye, Ebrahim Safdarian +1
We present a fast and scalable framework, leveraging graph neural networks (GNNs) and hierarchical matrix (-matrix) techniques, for simulating large-scale particulate…
Discrete Variable Topology Optimization Using Multi-Cut Formulation and Adaptive Trust Regions
Zisheng Ye, Wenxiao Pan
We present a new framework for solving general topology optimization (TO) problems that find an optimal material distribution within a design space to maximize the performance of a…
Nanoscale Imaging of Phonons and Reconfiguration in Topologically-Engineered, Self-Assembled Nanoparticle Lattice
Chang Qian, Ethan Stanifer, Zhan Ma +6
Topologically-engineered mechanical frames are important model constructs for architecture, machine mechanisms, and metamaterials. Despite significant advances in macroscopically f…