3D photorealistic scientific visualization of tsunami waves and sea level rise

Giannakidis, Alexandros; Giakoumidakis, Giannis; Mania, Katerina

doi:10.1109/ist.2014.6958467

Cited by 6 publications

(6 citation statements)

References 3 publications

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“…Though the present work focuses on molecular visualization, it also suggests how machine learning algorithms can rapidly and effectively enhance scientific visualization generally. Blender specifically has been used to visualize many scientific phenomena, ranging from quantum wave functions [28] to tsunami hydrodynamics [29] to astrophysical data [30,31]. A similar approach—generating simple representations of scientific data and converting those representations to higher-quality images—could be fruitfully applied in these other domains as well.…”

Section: Discussionmentioning

confidence: 99%

“…Blender specifically has been used to visualize many scientific phenomena, ranging from quantum wave functions [28] to tsunami hydrodynamics [29] to astrophysical data [30,31]. A similar approach-generating simple representations of scientific data and converting those representations to higher-quality images-could be fruitfully applied in these other domains as well.…”

Section: Discussionmentioning

confidence: 99%

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Prot2Prot: A Deep Learning Model for Rapid, Photorealistic Macromolecular Visualization

Durrant

2022

Preprint

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Motivation: Molecular visualization is a cornerstone of structural biology, providing insights into the form and function of biomolecules that are difficult to achieve any other way. Scientific analysis, publication, education, and outreach often benefit from photorealistic molecular depictions rendered using advanced computer-graphics programs such as Maya, 3ds Max, and Blender. However, setting up molecular scenes in these programs can be laborious even for expert users, and rendering often requires substantial time and computer resources. Results: We have created a deep-learning model called Prot2Prot that quickly imitates photorealistic visualization styles, given a much simpler, easy-to-generate molecular representation. The resulting images are often indistinguishable from images rendered using industry-standard 3D graphics programs, but they can be created in a fraction of the time, even when running in a web browser. To the best of our knowledge, Prot2Prot is the first example of image-to-image translation applied to macromolecular visualization. Availability: Prot2Prot is available free of charge, released under the terms of the Apache License, Version 2.0. Users can access a Prot2Prot-powered web app without registration at http://durrantlab.com/prot2prot.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Prot2Prot: A Deep Learning Model for Rapid, Photorealistic Macromolecular Visualization

Durrant

2022

Preprint

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“…Wave intensity can be calculated via the Dyer function of wave power that uses components such as wave height, gravity, and density [28]. Wave actions can be modeled using historical data [29], and numerical simulators such as SWAN [26,[30][31][32], TOMAWAC [26], XBeach [27], Method of Splitting Tsunami (MOST) [33], or forecasting models [26,32]. Google Earth engines and satellite images were used to monitor the dynamics of seawater via an open-source Python-based tool PyGEE-SWToolbox [34].…”

Section: Climatic Driversmentioning

confidence: 99%

“…Historical evidence in the city of Venice in Italy showed that vertical land movement caused by human activities could significantly contribute to a rise in RMSL, further worsening the hazard posed by SLR and extreme weather events caused by climate change [48]. In Greece, massive destructive tsunami waves that impacted the coastal areas occurred post-earthquake on Crete island [33]. It is also predicted that coastal areas in the Arabian Gulf will be affected by post-earthquake tsunami waves.…”

Section: Non-climatic Driversmentioning

confidence: 99%

“…The flooded areas were covered using a painter model to provide a realistic flooding scenario [94]. In [33], an extreme SLR scenario that occurred in the Mediterranean Sea in 350 AD was re-modeled to visualize how it would impact the current Heraklion port in Crete, Greece. Spatial data that contained topographic, bathymetry, and LU data were fed alongside modeled SLR and tsunami wave scenarios into an animation suite to build a 3D visualization tool with a scientific output.…”

Section: Coastal Assessment 41 Coastal Impact Modellingmentioning

confidence: 99%

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Exploring Key Aspects of Sea Level Rise and Their Implications: An Overview

Elneel,

Zitouni,

Mukhtar

et al. 2024

Water

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Sea level rise (SLR) is one of the most pressing challenges of climate change and has drawn noticeable research interest over the past few decades. Factors induced by global climate change, such as temperature increase, have resulted in both direct and indirect changes in sea levels at different spatial scales. Various climatic and non-climatic events contribute to sea level changes, posing risks to coastal and low-lying areas. Nevertheless, changes in sea level are not uniformly distributed globally due to several regional factors such as wave actions, storm surge frequencies, and tectonic land movement. The high exposure to those factors increases the vulnerability of subjected areas to SLR impacts. The impacts of events induced by climate change and SLR are reflected in biophysical, socioeconomic, and environmental aspects. Different indicator-based and model-based approaches are used to assess coastal areas’ vulnerabilities, response to impacts, and implementation of adaptation and mitigation measures. Various studies have been conducted to project future SLR impacts and evaluate implemented protection and adaptation approaches, aiding policymakers in planning effective adaptation and mitigation measures to reduce damage. This paper provides an overview of SLR and its key elements, encompassing contributing factors, impacts, and mitigation and adaptation measures, featuring a dedicated section on the Arabian Gulf, a semi-enclosed sea.

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Prot2Prot: a deep learning model for rapid, photorealistic macromolecular visualization

Durrant

2022

J Comput Aided Mol Des

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Molecular visualization is a cornerstone of structural biology, providing insights into the form and function of biomolecules that are difficult to achieve any other way. Scientific analysis, publication, education, and outreach often benefit from photorealistic molecular depictions rendered using advanced computer-graphics programs such as Maya, 3ds Max, and Blender. However, setting up molecular scenes in these programs is laborious even for expert users, and rendering often requires substantial time and computer resources. We have created a deep-learning model called Prot2Prot that quickly imitates photorealistic visualization styles, given a much simpler, easy-to-generate molecular representation. The resulting images are often indistinguishable from images rendered using industry-standard 3D graphics programs, but they can be created in a fraction of the time, even when running in a web browser. To the best of our knowledge, Prot2Prot is the first example of image-to-image translation applied to macromolecular visualization. Prot2Prot is available free of charge, released under the terms of the Apache License, Version 2.0. Users can access a Prot2Prot-powered web app without registration at http://durrantlab.com/prot2prot.

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3D photorealistic scientific visualization of tsunami waves and sea level rise

Cited by 6 publications

References 3 publications

Prot2Prot: A Deep Learning Model for Rapid, Photorealistic Macromolecular Visualization

Prot2Prot: A Deep Learning Model for Rapid, Photorealistic Macromolecular Visualization

Exploring Key Aspects of Sea Level Rise and Their Implications: An Overview

Prot2Prot: a deep learning model for rapid, photorealistic macromolecular visualization

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