Zobrazeno 1 - 10
of 89
pro vyhledávání: '"GHYSELS, PIETER"'
Autor:
Jin, Hongwei, Balaprakash, Prasanna, Zou, Allen, Ghysels, Pieter, Krishnapriyan, Aditi S., Mate, Adam, Barnes, Arthur, Bent, Russell
The threat of geomagnetic disturbances (GMDs) to the reliable operation of the bulk energy system has spurred the development of effective strategies for mitigating their impacts. One such approach involves placing transformer neutral blocking device
Externí odkaz:
http://arxiv.org/abs/2405.10389
Autor:
Khedkar, Kaustubh, Mamaghani, Amirreza Charchi, Ghysels, Pieter, Patankar, Neelesh A., Bhalla, Amneet Pal Singh
For decades, the computational multiphase flow community has grappled with mass loss in the level set method. Numerous solutions have been proposed, from fixing the reinitialization step to combining the level set method with other conservative schem
Externí odkaz:
http://arxiv.org/abs/2404.03132
The volume penalization (VP) or the Brinkman penalization (BP) method is a diffuse interface method for simulating multiphase fluid-structure interaction (FSI) problems in ocean engineering and/or phase change problems in thermal sciences. The method
Externí odkaz:
http://arxiv.org/abs/2306.06277
We extend an adaptive partially matrix-free Hierarchically Semi-Separable (HSS) matrix construction algorithm by Gorman et al. [SIAM J. Sci. Comput. 41(5), 2019] which uses Gaussian sketching operators to a broader class of Johnson--Lindenstrauss (JL
Externí odkaz:
http://arxiv.org/abs/2302.01977
Autor:
Khedkar, Kaustubh, Mamaghani, Amirreza Charchi, Ghysels, Pieter, Patankar, Neelesh A., Bhalla, Amneet Pal Singh
Publikováno v:
In Journal of Computational Physics 1 January 2025 520
We present a graph bisection and partitioning algorithm based on graph neural networks. For each node in the graph, the network outputs probabilities for each of the partitions. The graph neural network consists of two modules: an embedding phase and
Externí odkaz:
http://arxiv.org/abs/2110.08614
Graph Partitioning and Sparse Matrix Ordering using Reinforcement Learning and Graph Neural Networks
We present a novel method for graph partitioning, based on reinforcement learning and graph convolutional neural networks. Our approach is to recursively partition coarser representations of a given graph. The neural network is implemented using SAGE
Externí odkaz:
http://arxiv.org/abs/2104.03546
Publikováno v:
SIAM Journal on Scientific Computing. 2021(0):S367-91
We present a fast and approximate multifrontal solver for large-scale sparse linear systems arising from finite-difference, finite-volume or finite-element discretization of high-frequency wave equations. The proposed solver leverages the butterfly a
Externí odkaz:
http://arxiv.org/abs/2007.00202
This paper presents an adaptive randomized algorithm for computing the butterfly factorization of a $m\times n$ matrix with $m\approx n$ provided that both the matrix and its transpose can be rapidly applied to arbitrary vectors. The resulting factor
Externí odkaz:
http://arxiv.org/abs/2002.03400
This paper presents performance results comparing MPI-based implementations of the popular Conjugate Gradient (CG) method and several of its communication hiding (or 'pipelined') variants. Pipelined CG methods are designed to efficiently solve SPD li
Externí odkaz:
http://arxiv.org/abs/1905.06850