12 papers
VesNet: Neural network accelerated solver for simulating Stokesian vesicle suspensions
Shan Zhong, Gokberk Kabacaoglu, George Biros
Numerical simulation of deformable particle suspensions in Stokes flow is computationally expensive due to nonlinear fluid-structure interactions, evolving interfaces, and multisca…
A performance portable fast Ewald summation for Stokes flow
Gabriel Kosmacher, Ziyu Du, Joar Bagge +1
We present GPU algorithms for Ewald summation methods for accelerating N-body Stokes flow problems in periodic domains. Like most N-body codes, Ewald sums use a near-field/far-fiel…
IV-Net: A neural network for elliptic PDEs with random and highly varying coefficients
Shan Zhong, George Biros
We introduce a novel neural operator architecture designed to approximate solutions of linear elliptic partial differential equations with high-contrast, spatially varying coeffici…
VLCs: Managing Parallelism with Virtualized Libraries
Yineng Yan, William Ruys, Hochan Lee +11
As the complexity and scale of modern parallel machines continue to grow, programmers increasingly rely on composition of software libraries to encapsulate and exploit parallelism.…
Proximal-IMH: Proximal Posterior Proposals for Independent Metropolis-Hastings with Approximate Operators
Youguang Chen, George Biros
We consider the problem of sampling from a posterior distribution arising in Bayesian inverse problems in science, engineering, and imaging. Our method belongs to the family of ind…
Latent-IMH: Efficient Bayesian Inference for Inverse Problems with Approximate Operators
Youguang Chen, George Biros
We study sampling from posterior distributions in Bayesian linear inverse problems where , the parameters to observables operator, is computationally expensive. In many applicat…