{"ID":2862729,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.26139","arxiv_id":"2509.26139","title":"Leveraging AI modelling for FDS with Simvue: monitor and optimise for more sustainable simulations","abstract":"There is high demand on fire simulations, in both scale and quantity. We present a multi-pronged approach to improving the time and energy required to meet these demands. We show the ability of a custom machine learning surrogate model to predict the dynamics of heat propagation orders of magnitude faster than state-of-the-art CFD software for this application. We also demonstrate how a guided optimisation procedure can decrease the number of simulations required to meet an objective; using lightweight models to decide which simulations to run, we see a tenfold reduction when locating the most dangerous location for a fire to occur within a building based on the impact of smoke on visibility. Finally we present a framework and product, Simvue, through which we access these tools along with a host of automatic organisational and tracking features which enables future reuse of data and more savings through better management of simulations and combating redundancy.","short_abstract":"There is high demand on fire simulations, in both scale and quantity. We present a multi-pronged approach to improving the time and energy required to meet these demands. We show the ability of a custom machine learning surrogate model to predict the dynamics of heat propagation orders of magnitude faster than state-of...","url_abs":"https://arxiv.org/abs/2509.26139","url_pdf":"https://arxiv.org/pdf/2509.26139v1","authors":"[\"James Panayis\",\"Matt Field\",\"Vignesh Gopakumar\",\"Andrew Lahiff\",\"Kristian Zarebski\",\"Aby Abraham\",\"Jonathan L. Hodges\"]","published":"2025-09-30T11:57:38Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"cs.AI\",\"physics.comp-ph\"]","methods":"[\"Generative Adversarial Network\"]","has_code":false}