A fast and efficient MATLAB-based MPM solver:  fMPMM-solver v1.1

Wyser, Emmanuel; Alkhimenkov, Yury; Jaboyedoff, Michel; Podladchikov, Yury Y.

doi:https://doi.org/10.5194/gmd-13-6265-2020

Articles | Volume 13, issue 12

https://doi.org/10.5194/gmd-13-6265-2020

© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/gmd-13-6265-2020

© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 13, issue 12

Model description paper

|

10 Dec 2020

Model description paper |

| 10 Dec 2020

A fast and efficient MATLAB-based MPM solver: fMPMM-solver v1.1

Emmanuel Wyser, Yury Alkhimenkov, Michel Jaboyedoff, and Yury Y. Podladchikov

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Final revised paper (published on 10 Dec 2020)
Preprint (discussion started on 20 Jul 2020)

Interactive discussion

Status: closed

AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment

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- Supplement

RC1: 'Review', Anonymous Referee #1, 11 Aug 2020
- AC1: 'Response to Referee 1', Emmanuel Wyser, 14 Aug 2020
RC2: 'Review', Anonymous Referee #2, 24 Aug 2020
- AC2: 'Response to Referee 2', Emmanuel Wyser, 01 Sep 2020

Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision

AR by Emmanuel Wyser on behalf of the Authors (12 Oct 2020) Author's response Manuscript

ED: Referee Nomination & Report Request started (13 Oct 2020) by Alexander Robel

RR by Anonymous Referee #2 (26 Oct 2020)

ED: Publish subject to technical corrections (27 Oct 2020) by Alexander Robel

AR by Emmanuel Wyser on behalf of the Authors (28 Oct 2020) Manuscript

Short summary

In this work, we present an efficient and fast material point method (MPM) implementation in MATLAB. We first discuss the vectorization strategies to adapt this numerical method to a MATLAB implementation. We report excellent agreement of the solver compared with classical analysis among the MPM community, such as the cantilever beam problem. The solver achieves a performance gain of 28 compared with a classical iterative implementation.