Cluster analysis for granular mechanics simulations using Machine Learning Algorithms

Authors

DOI:

https://doi.org/10.31908/19098367.2058

Keywords:

granular simulations, machine learning, classification analysis, performance analysis

Abstract

Molecular Dynamics (MD) simulations on grain collisions allow to incorporate complex properties of dust interactions. We performed simulations of collisions of porous grains, each with many particles, using the MD software LAMMPS. The simulations consisted of a projectile grain striking a larger immobile target grain, with different impact velocities. The disadvantage of this method is the large computational cost due to a large number of particles being modeled. Machine Learning (ML) has the power to manipulate large data and build predictive models which could reduce MD simulation times. Using ML algorithms (Support Vector Machine and Random Forest) we are able to predict the outcome of MD simulations regarding fragment formation, after a number of steps smaller than in usual MD simulations. We achieved a time reduction of at least 46%, for 90% accuracy. These results show that SVM and RF can be powerful yet simple tools to reduce computational cost in collision fragmentation simulations.

Author Biographies

  • Daniela Noemi Rim, Universidad Nacional de Cuyo

    Bachelor’s degree in Basic Sciences with Orientation in Physics (Faculty of Exact and Natural Sciences, National University of Cuyo, Mendoza, Argentina, emitted April 17 th 2019). Thesis title: ‘Machine Learning techniques applied to Numerical Simulation of Granular Porous Materials’. Currently studying an MS in South Korea (‘MS in Information Technology’, Department of Information and Communication Engineering, Handong Global University, Pohang, Republic of Korea, since February 2020). Member of research group MILab (Machine Intelligence Lab, Handong Global University, Pohang, Republic of Korea). The topic of research focuses in Deep Learning applied to Natural Language Processing tasks (such as Neural Machine Translations) and audio signal compressions using Variational Autoencoders.

  • Emmanuel N. Millán, Universidad Nacional de Cuyo

    Received his Ph.D. degree from Universidad Nacional de San Luis (UNSL), Argentina in 2016, and a BSc. in Software Engineering from Universidad del Aconcagua, Argentina, in 2010. He is a researcher within CONICET. He is interested in the implementation of parallel problems in hybrid clusters including Graphics Processing Units (GPUs), with applications in Molecular Dynamics, Machine Learning, Cellular Automata and Monte Carlo methods.

  • María Belén Planes, Universidad de Mendoza

    2016 “Licenciada en Ciencias Básicas con orientación en Física” FCEN – Universidad Nacional de Cuyo – Mendoza, Argentina. 2018 “Profesora de grado universitario en Ciencias Básicas con orientación en Física” FCEN – Universidad Nacional de Cuyo – Mendoza, Argentina. 2020 last-year student “Doctorado en Astronomía” FCEFN - Universidad Nacional de San Juan – San Juan, Argentina. Dust aggregate collisions have been studied through complex molecular dynamics simulations with a focus on astrophysical topics that are not currently understood, such as the formation of planets in their early stage, high speed collisions in debris discs and the evolution of dust emitted by comets in their internal coma. [Planes M. B., et al, A&A 607, A19 (2017)] [Planes M. B., et al, MNRAS: Letters 487, L13 (2019)] [Planes M. B., et al, MNRAS 492, 1937 (2020)]. SIMAF – Universidad de Mendoza. CONICET – Argentina Research areas: planetary formation – comets – granular mechanics.

  • Eduardo M. Bringa, Universidad de Mendoza

    Ph.D. Physics. 2000. University of Virginia (UVa), Charlottesville, USA. 1994. Licenciado en Física, Instituto Balseiro, Bariloche, Argentina. After obtaining his Ph.D., he was a postdoctoral researcher at the Astronomy Department (UVa, 2000-2001), and then postdoctoral researcher at Lawrence Livermore National Laboratory (LLNL, 2001-2003), where he later became part of the permanent research staff. In 2008 he returned to Argentina, where he currently is Principal Researcher in CONICET at the “Universidad de Mendoza”, and full Professor at “Universidad Nacional de Cuyo”. He is a member of AFA (Argentinean Physical Society) and APS (American Physical Society). Research area: simulations in physics, astrophysics and materials sciences, including Molecular Dynamics, Spin Dynamics and Monte Carlo simulations.

  • Luis G. Moyano, Universidad Nacional de Cuyo

    Is an adjunct researcher at CONICET and associate professor at Instituto Balseiro (UNCuyo/Comisión Nacional de Energía Atómica). He is invited professor at Facultad de Ciencias Exactas y Naturales, UNCuyo. He graduated in Physics from Instituto Balseiro (Bariloche, Argentina, 2000) and holds a PhD also in Physics from CBPF/UFRJ (Rio de Janeiro, Brazil, 2006). Dr. Moyano was research staff member at IBM Research Brazil until 2016. Prior to working at IBM, he was leader data scientist at BBVA Data & Analytics (Madrid, Spain). He was also staff researcher at Telefónica Research (2008-2013, Madrid/Barcelona). He is currently a permanent member of the Statistical and Interdisciplinary Physics Group at Centro Atómico Bariloche. Dr. Luis Gregorio Moyano research interests lie in the intersection of physics and machine learning. His lines of work include network representation learning, with applications to biological and social systems

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Published

2020-12-21

Issue

Vol. 14 No. 28 (2020)

Section

Artículos

How to Cite

Cluster analysis for granular mechanics simulations using Machine Learning Algorithms. (2020). Entre Ciencia E ingeniería, 14(28), 81-86. https://doi.org/10.31908/19098367.2058