activity
20142025
most citedQuantification of mixing in vesicle suspensions using numerical simulations in two dimensions

14 citations · 15 across the 6 of their papers we have counts for

collaborators

6 papers

physics.plasm-ph2025

Boltzsim: A fast solver for the 1D-space electron Boltzmann equation with applications to radio-frequency glow discharge plasmas

Milinda Fernando, James Almgren-Bell, Todd Oliver +4

We present an algorithm for solving the one-dimensional space collisional Boltzmann transport equation (BTE) for electrons in low-temperature plasmas (LTPs). Modeling LTPs is usefu…

math.NA2024

Inverse Problem Regularization for 3D Multi-Species Tumor Growth Models

Ali Ghafouri, George Biros

We present a multi-species partial differential equation (PDE) model for tumor growth and a an algorithm for calibrating the model from magnetic resonance imaging (MRI) scans. The…

cs.CE2024

GrainGNN: A dynamic graph neural network for predicting 3D grain microstructure

Yigong Qin, Stephen DeWitt, Balasubramaniam Radhakrishnan +1

We propose GrainGNN, a surrogate model for the evolution of polycrystalline grain structure under rapid solidification conditions in metal additive manufacturing. High fidelity sim…

physics.flu-dyn201614 cited

Quantification of mixing in vesicle suspensions using numerical simulations in two dimensions

Gokberk Kabacaoglu, Bryan Quaife, George Biros

We study mixing in Stokesian vesicle suspensions in two dimensions on a cylindrical Couette apparatus using numerical simulations. The vesicle flow simulation is done using a bound…

cs.DS2014

ASKIT: Approximate Skeletonization Kernel-Independent Treecode in High Dimensions

William B. March, Bo Xiao, George Biros

We present a fast algorithm for kernel summation problems in high-dimensions. These problems appear in computational physics, numerical approximation, non-parametric statistics, an…

cs.LG20141 cited

Far-Field Compression for Fast Kernel Summation Methods in High Dimensions

William B. March, George Biros

We consider fast kernel summations in high dimensions: given a large set of points in dimensions (with ) and a pair-potential function (the {\em kernel} function), we…