2023
DOI: 10.1016/j.future.2023.04.006
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The EU Center of Excellence for Exascale in Solid Earth (ChEESE): Implementation, results, and roadmap for the second phase

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Cited by 8 publications
(4 citation statements)
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“…Looking ahead, one can anticipate the development of more advanced methods for handling the uncertainties in seismic hazard datasets, components and models. These methods are likely to involve the use of physicsbased simulations of both earthquake ruptures and/or ground shaking (Bradley, 2019;Paolucci et al, 2021;Li et al, 2023).…”
Section: Discussionmentioning
confidence: 99%
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“…Looking ahead, one can anticipate the development of more advanced methods for handling the uncertainties in seismic hazard datasets, components and models. These methods are likely to involve the use of physicsbased simulations of both earthquake ruptures and/or ground shaking (Bradley, 2019;Paolucci et al, 2021;Li et al, 2023).…”
Section: Discussionmentioning
confidence: 99%
“…The integration of physics-based simulations into seismic hazard modelling has the potential to improve the main pool of existing records, enhance ground shaking characterization particularly in the magnitude-distance range poorly covered in current strong-motion databases, improve the ground motion characteristic models, augment our understanding of earthquake scenarios, and support earthquake preparedness and mitigation strategies (Graves et al, 2011). In addition to using physicsbased simulations, one can also anticipate significant progress in computational capabilities, such as high-performance computing (Folch et al, 2023), and artificial intelligence (AI)-driven analytics in the coming years. These leading-edge tools have the potential to significantly accelerate the processing of extensive, multidisciplinary datasets and complex calculations (Dal Zilio et al, 2023).…”
Section: Discussionmentioning
confidence: 99%
“…Two main challenges are (i) building interdisciplinary groups and ensuring effective interactions between the disciplinary experts, and (ii) engaging with civil society (a structured and sometimes lengthy process) by building trust between scientists and stakeholders (UNDRR, 2022b). Research infrastructures can foster the development of a transdisciplinary research community in the field of disaster risk (Peek et al, 2020) and provide powerful tools (e.g., data, codes, expertise) to research groups (e.g., Folch et al, 2023;Calatrava et al, 2023;Dañobeitia et al, 2020). Access and interaction with research infrastructures should therefore be promoted and encouraged among the disaster risk community to exploit these opportunities.…”
Section: Transdisciplinaritymentioning
confidence: 99%
“…Approximations of results using methods like Green's Function, amplification factors, and reduced complexity models (Molinari et al, 2016;Løvholt et al, 2016;Glimsdal et al, 2019;Gailler et al, 2018;Grzan et al, 2021;Röbke et al, 2021) 3. Hardware and computational improvements like a nesting of grid domains, adaptive variable grid resolution, parallelisation and GPU-based acceleration, coupled multi-scale modelling, exascale codes (LeVeque et al, 2011;Shi et al, 2012;Oishi et al, 2015;Macías et al, 2017;Marras and Mandli, 2021;Folch et al, 2023) 4. Surrogates using statistical emulators and machine learning models (Sarri et al, 2012;Salmanidou et al, 2021;Mulia et al, 2018;Fauzi and Mizutani, 2019;Makinoshima et al, 2021;Fukutani et al, 2023;Mulia et al, 2022) Running the numerical simulations, especially for modelling the tsunami inundation with an NLSWE model takes a lot of time and computational resources, for example accounting for about 13,600 GPU hours in the local PTHA study for Catanai by Gibbons et al (2020), refer Table S5 for runtimes of this study.…”
mentioning
confidence: 99%