Scalable and modular material point method for large-scale simulations
September 29, 2019 Β· Declared Dead Β· π arXiv.org
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Krishna Kumar, Jeffrey Salmond, Shyamini Kularathna, Christopher Wilkes, Ezra Tjung, Giovanna Biscontin, Kenichi Soga
arXiv ID
1909.13380
Category
physics.geo-ph
Cross-listed
cs.DC
Citations
35
Venue
arXiv.org
Last Checked
3 months ago
Abstract
In this paper, we describe a new scalable and modular material point method (MPM) code developed for solving large-scale problems in continuum mechanics. The MPM is a hybrid Eulerian-Lagrangian approach, which uses both moving material points and computational nodes on a background mesh. The MPM has been successfully applied to solve large-deformation problems such as landslides, failure of slopes, concrete flows, etc. Solving these large-deformation problems result in the material points actively moving through the mesh. Developing an efficient parallelisation scheme for the MPM code requires dynamic load-balancing techniques for both the material points and the background mesh. This paper describes the data structures and algorithms employed to improve the performance and portability of the MPM code. An object-oriented programming paradigm is adopted to modularise the MPM code. The Unified Modelling Language (UML) diagram of the MPM code structure is shown in Figure 1.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β physics.geo-ph
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
A Machine-Learning Approach for Earthquake Magnitude Estimation
R.I.P.
π»
Ghosted
Uncertainty Estimation with Deep Learning for Rainfall-Runoff Modelling
R.I.P.
π»
Ghosted
Bayesian-Deep-Learning Estimation of Earthquake Location from Single-Station Observations
R.I.P.
π»
Ghosted
Convolutional Neural Network for Convective Storm Nowcasting Using 3D Doppler Weather Radar Data
R.I.P.
π»
Ghosted
Seismic data interpolation based on U-net with texture loss
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted