DataStorm-EM: Exploration of Alternative Timelines within Continuous-Coupled Simulation Ensembles
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arXiv
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| Auteurs principaux: | , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866911963506802688 |
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| author | Azad, Fahim Tasneema Anton, Javier Redondo Mitra, Shubhodeep Singh, Fateh Behrens, Hans Li, Mao-Lin Arslan, Bilgehan Candan, K. Selçuk Sapino, Maria Luisa |
| author_facet | Azad, Fahim Tasneema Anton, Javier Redondo Mitra, Shubhodeep Singh, Fateh Behrens, Hans Li, Mao-Lin Arslan, Bilgehan Candan, K. Selçuk Sapino, Maria Luisa |
| contents | Many socio-economical critical domains (such as sustainability, public health, and disasters) are characterized by highly complex and dynamic systems, requiring data and model-driven simulations to support decision-making. Due to a large number of unknowns, decision-makers usually need to generate ensembles of stochastic scenarios, requiring hundreds or thousands of individual simulation instances, each with different parameter settings corresponding to distinct scenarios, As the number of model parameters increases, the number of potential timelines one can simulate increases exponentially. Consequently, simulation ensembles are inherently sparse, even when they are extremely large. This necessitates a platform for (a) deciding which simulation instances to execute and (b) given a large simulation ensemble, enabling decision-makers to explore the resulting alternative timelines, by extracting and visualizing consistent, yet diverse timelines from continuous-coupled simulation ensembles. In this article, we present DataStorm-EM platform for data- and model-driven simulation ensemble management, optimization, analysis, and exploration, describe underlying challenges and present our solution. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_14571 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | DataStorm-EM: Exploration of Alternative Timelines within Continuous-Coupled Simulation Ensembles Azad, Fahim Tasneema Anton, Javier Redondo Mitra, Shubhodeep Singh, Fateh Behrens, Hans Li, Mao-Lin Arslan, Bilgehan Candan, K. Selçuk Sapino, Maria Luisa Multiagent Systems Many socio-economical critical domains (such as sustainability, public health, and disasters) are characterized by highly complex and dynamic systems, requiring data and model-driven simulations to support decision-making. Due to a large number of unknowns, decision-makers usually need to generate ensembles of stochastic scenarios, requiring hundreds or thousands of individual simulation instances, each with different parameter settings corresponding to distinct scenarios, As the number of model parameters increases, the number of potential timelines one can simulate increases exponentially. Consequently, simulation ensembles are inherently sparse, even when they are extremely large. This necessitates a platform for (a) deciding which simulation instances to execute and (b) given a large simulation ensemble, enabling decision-makers to explore the resulting alternative timelines, by extracting and visualizing consistent, yet diverse timelines from continuous-coupled simulation ensembles. In this article, we present DataStorm-EM platform for data- and model-driven simulation ensemble management, optimization, analysis, and exploration, describe underlying challenges and present our solution. |
| title | DataStorm-EM: Exploration of Alternative Timelines within Continuous-Coupled Simulation Ensembles |
| topic | Multiagent Systems |
| url | https://arxiv.org/abs/2407.14571 |