Three-dimensional simulations of oblique asteroid impacts into water

Gisler, G.; Heberling, T.; Plesko, C. S.; Weaver, Robert P.

doi:10.1016/j.jsse.2018.06.001

Cited by 6 publications

(3 citation statements)

References 10 publications

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“…The first real‐world dataset that we analyze is the Deep Water Asteroid Impact dataset [GHPW18]. In this ensemble, three simulation parameters (asteroid size, impact angle, airburst) have been varied to study the extent of an asteroid impacting deep‐sea oceans.…”

Section: Resultsmentioning

confidence: 99%

SimilarityNet: A Deep Neural Network for Similarity Analysis Within Spatio‐temporal Ensembles

Huesmann

Linsen

2022

Computer Graphics Forum

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Latent feature spaces of deep neural networks are frequently used to effectively capture semantic characteristics of a given dataset. In the context of spatio‐temporal ensemble data, the latent space represents a similarity space without the need of an explicit definition of a field similarity measure. Commonly, these networks are trained for specific data within a targeted application. We instead propose a general training strategy in conjunction with a deep neural network architecture, which is readily applicable to any spatio‐temporal ensemble data without re‐training. The latent‐space visualization allows for a comprehensive visual analysis of patterns and temporal evolution within the ensemble. With the use of SimilarityNet, we are able to perform similarity analyses on large‐scale spatio‐temporal ensembles in less than a second on commodity consumer hardware. We qualitatively compare our results to visualizations with established field similarity measures to document the interpretability of our latent space visualizations and show that they are feasible for an in‐depth basic understanding of the underlying temporal evolution of a given ensemble.

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Section: Resultsmentioning

confidence: 99%

SimilarityNet: A Deep Neural Network for Similarity Analysis Within Spatio‐temporal Ensembles

Huesmann

Linsen

2022

Computer Graphics Forum

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“…The value of v02 lies between 0.0 and 1.0, where 1.0 means pure water. The goal is to study the phenomena of ablation and ejecta material as the asteroid enters and subsequently impacts the water, sending a plume of material into the surrounding area and up into the atmosphere [ 69 ]. Since the temperature increases as the asteroid enters the atmosphere, in this work, we perform the following query to study the interaction between tev and v02: 0.13 < tev < 0.5 AND 0.45 < v02 < 1.0.…”

Section: Resultsmentioning

confidence: 99%

Multivariate Pointwise Information-Driven Data Sampling and Visualization

Dutta

Biswas

Ahrens

2019

Entropy

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With increasing computing capabilities of modern supercomputers, the size of the data generated from the scientific simulations is growing rapidly. As a result, application scientists need effective data summarization techniques that can reduce large-scale multivariate spatiotemporal data sets while preserving the important data properties so that the reduced data can answer domain-specific queries involving multiple variables with sufficient accuracy. While analyzing complex scientific events, domain experts often analyze and visualize two or more variables together to obtain a better understanding of the characteristics of the data features. Therefore, data summarization techniques are required to analyze multi-variable relationships in detail and then perform data reduction such that the important features involving multiple variables are preserved in the reduced data. To achieve this, in this work, we propose a data sub-sampling algorithm for performing statistical data summarization that leverages pointwise information theoretic measures to quantify the statistical association of data points considering multiple variables and generates a sub-sampled data that preserves the statistical association among multi-variables. Using such reduced sampled data, we show that multivariate feature query and analysis can be done effectively. The efficacy of the proposed multivariate association driven sampling algorithm is presented by applying it on several scientific data sets.

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“…Airburst asteroids represent the third class, recently investigated by Gisler et al (2017) and Patchett et al (2017). They pointed out that (1) airbursts happen when the asteroids interaction with the atmosphere causes Reviews of Geophysics 10.1002/2017RG000579 it to explode before impacting the surface provoking the transition of kinetic energy from the asteroid into the water to be more coherent and changing the likelihood of tsunami generation and (2) a more oblique angle increases the amount of time the asteroid spends in the atmosphere and therefore increases the amount of ablation opportunity before the impact (possibly making a smaller impactor) which could possibly push the water in a more coherent direction increasing the likelihood of tsunami generation.…”

Section: Asteroid Impactsmentioning

confidence: 99%

Probabilistic Tsunami Hazard Analysis: Multiple Sources and Global Applications

Grezio

Babeyko²,

Baptista

et al. 2017

Reviews of Geophysics

211

165

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Applying probabilistic methods to infrequent but devastating natural events is intrinsically challenging. For tsunami analyses, a suite of geophysical assessments should be in principle evaluated because of the different causes generating tsunamis (earthquakes, landslides, volcanic activity, meteorological events, and asteroid impacts) with varying mean recurrence rates. Probabilistic Tsunami Hazard Analyses (PTHAs) are conducted in different areas of the world at global, regional, and local scales with the aim of understanding tsunami hazard to inform tsunami risk reduction activities. PTHAs enhance knowledge of the potential tsunamigenic threat by estimating the probability of exceeding specific levels of tsunami intensity metrics (e.g., run‐up or maximum inundation heights) within a certain period of time (exposure time) at given locations (target sites); these estimates can be summarized in hazard maps or hazard curves. This discussion presents a broad overview of PTHA, including (i) sources and mechanisms of tsunami generation, emphasizing the variety and complexity of the tsunami sources and their generation mechanisms, (ii) developments in modeling the propagation and impact of tsunami waves, and (iii) statistical procedures for tsunami hazard estimates that include the associated epistemic and aleatoric uncertainties. Key elements in understanding the potential tsunami hazard are discussed, in light of the rapid development of PTHA methods during the last decade and the globally distributed applications, including the importance of considering multiple sources, their relative intensities, probabilities of occurrence, and uncertainties in an integrated and consistent probabilistic framework.

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Three-dimensional simulations of oblique asteroid impacts into water

Cited by 6 publications

References 10 publications

SimilarityNet: A Deep Neural Network for Similarity Analysis Within Spatio‐temporal Ensembles

SimilarityNet: A Deep Neural Network for Similarity Analysis Within Spatio‐temporal Ensembles

Multivariate Pointwise Information-Driven Data Sampling and Visualization

Probabilistic Tsunami Hazard Analysis: Multiple Sources and Global Applications

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