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

doi:10.1007/978-3-030-91608-4_49

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

Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

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 language	English
Title of host publication	Intelligent Data Engineering and Automated Learning – IDEAL 2021
Subtitle of host publication	22nd International Conference, IDEAL 2021, Manchester, UK, November 25–27, 2021, Proceedings
Editors	Hujun Yin, David Camacho, Peter Tino, Richard Allmendinger, Antonio J. Tallón-Ballesteros, Ke Tang, Sung-Bae Cho, Paulo Novais, Susana Nascimento
Place of Publication	Cham
Publisher	Springer
Pages	493–501
Number of pages	9
Edition	1
ISBN (Electronic)	9783030916084
ISBN (Print)	9783030916077
DOIs	https://doi.org/10.1007/978-3-030-91608-4_49
Publication status	Published - 23 Nov 2021
Event	The 22nd International Conference on Intelligent Data Engineering and Automated Learning (IDEAL) - Manchester, United Kingdom Duration: 25 Nov 2021 → 27 Nov 2021 Conference number: 22nd

Publication series

Name	Lecture Notes in Computer Science
Publisher	Springer
Volume	13113
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	The 22nd International Conference on Intelligent Data Engineering and Automated Learning (IDEAL)
Abbreviated title	IDEAL 2021
Country/Territory	United Kingdom
City	Manchester
Period	25/11/21 → 27/11/21

Keywords

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

Access to Document

10.1007/978-3-030-91608-4_49

Cite this

Canducci, M., Taghribi, A., Mastropietro, M., Rijcke, S. D., Peletier, R., Bunte, K., & Tino, P. (2021). Tracking the temporal-evolution of supernova bubbles in numerical simulations. In H. Yin, D. Camacho, P. Tino, R. Allmendinger, A. J. Tallón-Ballesteros, K. Tang, S.-B. Cho, P. Novais, & S. Nascimento (Eds.), Intelligent Data Engineering and Automated Learning – IDEAL 2021: 22nd International Conference, IDEAL 2021, Manchester, UK, November 25–27, 2021, Proceedings (1 ed., pp. 493–501). (Lecture Notes in Computer Science ; Vol. 13113). Springer. https://doi.org/10.1007/978-3-030-91608-4_49

Canducci, Marco ; Taghribi, Abolfazl ; Mastropietro, Michele et al. / Tracking the temporal-evolution of supernova bubbles in numerical simulations. Intelligent Data Engineering and Automated Learning – IDEAL 2021: 22nd International Conference, IDEAL 2021, Manchester, UK, November 25–27, 2021, Proceedings. editor / Hujun Yin ; David Camacho ; Peter Tino ; Richard Allmendinger ; Antonio J. Tallón-Ballesteros ; Ke Tang ; Sung-Bae Cho ; Paulo Novais ; Susana Nascimento. 1. ed. Cham : Springer, 2021. pp. 493–501 (Lecture Notes in Computer Science ).

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title = "Tracking the temporal-evolution of supernova bubbles in numerical simulations",

abstract = "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.",

keywords = "Manifold learning, l1 and l2-regularization, Temporal generative topographic mapping, SPH simulation, Superbubble",

author = "Marco Canducci and Abolfazl Taghribi and Michele Mastropietro and Rijcke, {Sven de} and Reynier Peletier and Kerstin Bunte and Peter Tino",

year = "2021",

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doi = "10.1007/978-3-030-91608-4_49",

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editor = "Hujun Yin and David Camacho and Peter Tino and Richard Allmendinger and Tall{\'o}n-Ballesteros, {Antonio J.} and Ke Tang and Sung-Bae Cho and Paulo Novais and Susana Nascimento",

booktitle = "Intelligent Data Engineering and Automated Learning – IDEAL 2021",

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Canducci, M, Taghribi, A, Mastropietro, M, Rijcke, SD, Peletier, R, Bunte, K & Tino, P 2021, Tracking the temporal-evolution of supernova bubbles in numerical simulations. in H Yin, D Camacho, P Tino, R Allmendinger, AJ Tallón-Ballesteros, K Tang, S-B Cho, P Novais & S Nascimento (eds), Intelligent Data Engineering and Automated Learning – IDEAL 2021: 22nd International Conference, IDEAL 2021, Manchester, UK, November 25–27, 2021, Proceedings. 1 edn, Lecture Notes in Computer Science , vol. 13113, Springer, Cham, pp. 493–501, The 22nd International Conference on Intelligent Data Engineering and Automated Learning (IDEAL), Manchester, United Kingdom, 25/11/21. https://doi.org/10.1007/978-3-030-91608-4_49

Tracking the temporal-evolution of supernova bubbles in numerical simulations. / Canducci, Marco; Taghribi, Abolfazl; Mastropietro, Michele et al.
Intelligent Data Engineering and Automated Learning – IDEAL 2021: 22nd International Conference, IDEAL 2021, Manchester, UK, November 25–27, 2021, Proceedings. ed. / Hujun Yin; David Camacho; Peter Tino; Richard Allmendinger; Antonio J. Tallón-Ballesteros; Ke Tang; Sung-Bae Cho; Paulo Novais; Susana Nascimento. 1. ed. Cham: Springer, 2021. p. 493–501 (Lecture Notes in Computer Science ; Vol. 13113).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Tracking the temporal-evolution of supernova bubbles in numerical simulations

AU - Canducci, Marco

AU - Taghribi, Abolfazl

AU - Mastropietro, Michele

AU - Rijcke, Sven de

AU - Peletier, Reynier

AU - Bunte, Kerstin

AU - Tino, Peter

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N2 - 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.

AB - 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.

KW - Manifold learning

KW - l1 and l2-regularization

KW - Temporal generative topographic mapping

KW - SPH simulation

KW - Superbubble

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DO - 10.1007/978-3-030-91608-4_49

M3 - Conference contribution

SN - 9783030916077

T3 - Lecture Notes in Computer Science

SP - 493

EP - 501

BT - Intelligent Data Engineering and Automated Learning – IDEAL 2021

A2 - Yin, Hujun

A2 - Camacho, David

A2 - Tino, Peter

A2 - Allmendinger, Richard

A2 - Tallón-Ballesteros, Antonio J.

A2 - Tang, Ke

A2 - Cho, Sung-Bae

A2 - Novais, Paulo

A2 - Nascimento, Susana

PB - Springer

CY - Cham

T2 - The 22nd International Conference on Intelligent Data Engineering and Automated Learning (IDEAL)

Y2 - 25 November 2021 through 27 November 2021

ER -

Canducci M, Taghribi A, Mastropietro M, Rijcke SD, Peletier R, Bunte K et al. Tracking the temporal-evolution of supernova bubbles in numerical simulations. In Yin H, Camacho D, Tino P, Allmendinger R, Tallón-Ballesteros AJ, Tang K, Cho SB, Novais P, Nascimento S, editors, Intelligent Data Engineering and Automated Learning – IDEAL 2021: 22nd International Conference, IDEAL 2021, Manchester, UK, November 25–27, 2021, Proceedings. 1 ed. Cham: Springer. 2021. p. 493–501. (Lecture Notes in Computer Science ). doi: 10.1007/978-3-030-91608-4_49

Tracking the temporal-evolution of supernova bubbles in numerical simulations

Abstract

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Keywords

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