Tracking the temporal-evolution of supernova bubbles in numerical simulations

Marco Canducci, Abolfazl Taghribi, Michele Mastropietro, Sven de Rijcke, Reynier Peletier, Kerstin Bunte, Peter Tino

Research output: Chapter in Book/Report/Conference proceedingConference contribution


The study of low-dimensional, noisy manifolds embedded in a higher dimensional space has been extremely useful in many applications, from the chemical analysis of multi-phase flows to simulations of galactic mergers. Building a probabilistic model of the manifolds has helped in describing their essential properties and how they vary in space. However, when the manifold is evolving through time, a joint spatio-temporal modelling is needed, in order to fully comprehend its nature. We propose a first-order Markovian process that propagates the spatial probabilistic model of a manifold at fixed time, to its adjacent temporal stages. The proposed methodology is demonstrated using a particle simulation of an interacting dwarf galaxy to describe the evolution of a cavity generated by a Supernova.
Original languageEnglish
Title of host publicationIntelligent Data Engineering and Automated Learning – IDEAL 2021
Subtitle of host publication22nd International Conference, IDEAL 2021, Manchester, UK, November 25–27, 2021, Proceedings
EditorsHujun Yin, David Camacho, Peter Tino, Richard Allmendinger, Antonio J. Tallón-Ballesteros, Ke Tang, Sung-Bae Cho, Paulo Novais, Susana Nascimento
Place of PublicationCham
Number of pages9
ISBN (Electronic)9783030916084
ISBN (Print)9783030916077
Publication statusPublished - 23 Nov 2021
EventThe 22nd International Conference on Intelligent Data Engineering and Automated Learning (IDEAL) - Manchester, United Kingdom
Duration: 25 Nov 202127 Nov 2021
Conference number: 22nd

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


ConferenceThe 22nd International Conference on Intelligent Data Engineering and Automated Learning (IDEAL)
Abbreviated titleIDEAL 2021
Country/TerritoryUnited Kingdom


  • Manifold learning
  • l1 and l2-regularization
  • Temporal generative topographic mapping
  • SPH simulation
  • Superbubble


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