Tracking the Temporal-Evolution of Supernova Bubbles in Numerical Simulations

Marco Canducci; Albolfazl 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^*, Albolfazl Taghribi, Michele Mastropietro, Sven De Rijcke, Reynier Peletier, Kerstin Bunte, Peter Tino

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

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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
Editors	H. Yin
Place of Publication	Cham
Publisher	Springer
Pages	493-501
Number of pages	8
ISBN (Electronic)	978-3-030-91608-4
ISBN (Print)	978-3-030-91607-7
DOIs	https://doi.org/10.1007/978-3-030-91608-4_49
Publication status	Published - Nov-2021
Event	International Conference on Intelligent Data Engineering and Automated Learning: IDEAL 2021 - Manchester, United Kingdom Duration: 25-Nov-2021 → 27-Nov-2021

Publication series

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

Conference

Conference	International Conference on Intelligent Data Engineering and Automated Learning
Country/Territory	United Kingdom
City	Manchester
Period	25/11/2021 → 27/11/2021

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

Tracking the Temporal-Evolutionof Supernova Bubbles in Numerical SimulationsFinal publisher's version, 61 MBLicence: Taverne

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Canducci, M., Taghribi, A., Mastropietro, M., De Rijcke, S., Peletier, R., Bunte, K., & Tino, P. (2021). Tracking the Temporal-Evolution of Supernova Bubbles in Numerical Simulations. In H. Yin (Ed.), Intelligent Data Engineering and Automated Learning - IDEAL (pp. 493-501). (Lecture Notes in Computer Science; Vol. 13113). Springer. https://doi.org/10.1007/978-3-030-91608-4_49

@inproceedings{8999c50c9901461789acfb8f303ee96f,

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",

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

year = "2021",

month = nov,

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

language = "English",

isbn = "978-3-030-91607-7",

series = "Lecture Notes in Computer Science",

publisher = "Springer",

pages = "493--501",

editor = "\{Yin \}, \{H. \}",

booktitle = "Intelligent Data Engineering and Automated Learning - IDEAL",

note = "International Conference on Intelligent Data Engineering and Automated Learning : IDEAL 2021 ; Conference date: 25-11-2021 Through 27-11-2021",

}

Canducci, M, Taghribi, A, Mastropietro, M, De Rijcke, S, Peletier, R , Bunte, K & Tino, P 2021, Tracking the Temporal-Evolution of Supernova Bubbles in Numerical Simulations. in H Yin (ed.), Intelligent Data Engineering and Automated Learning - IDEAL. Lecture Notes in Computer Science, vol. 13113, Springer, Cham, pp. 493-501, International Conference on Intelligent Data Engineering and Automated Learning, Manchester, United Kingdom, 25/11/2021. https://doi.org/10.1007/978-3-030-91608-4_49

Tracking the Temporal-Evolution of Supernova Bubbles in Numerical Simulations. / Canducci, Marco; Taghribi, Albolfazl; Mastropietro, Michele et al.
Intelligent Data Engineering and Automated Learning - IDEAL. ed. / H. Yin . Cham: Springer, 2021. p. 493-501 (Lecture Notes in Computer Science; Vol. 13113).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

TY - GEN

T1 - Tracking the Temporal-Evolution of Supernova Bubbles in Numerical Simulations

AU - Canducci, Marco

AU - Taghribi, Albolfazl

AU - Mastropietro, Michele

AU - De Rijcke, Sven

AU - Peletier, Reynier

AU - Bunte, Kerstin

AU - Tino, Peter

PY - 2021/11

Y1 - 2021/11

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

U2 - 10.1007/978-3-030-91608-4_49

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

M3 - Conference contribution

SN - 978-3-030-91607-7

T3 - Lecture Notes in Computer Science

SP - 493

EP - 501

BT - Intelligent Data Engineering and Automated Learning - IDEAL

A2 - Yin , H.

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CY - Cham

T2 - International Conference on Intelligent Data Engineering and Automated Learning

Y2 - 25 November 2021 through 27 November 2021

ER -

Tracking the Temporal-Evolution of Supernova Bubbles in Numerical Simulations

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