Please note that this session was withdrawn and is no longer available in the respective programme. This withdrawal might have been the result of a merge with another session.
ITS1.4/NH13.10 | Hybrid modeling of natural hazards: blending deep-learning, data-driven approaches and physics-based simulations
EDI PICO
Hybrid modeling of natural hazards: blending deep-learning, data-driven approaches and physics-based simulations
Convener: Filippo Gatti | Co-convener: Nishtha SrivastavaECSECS
Recent advances in the field of Artificial Intelligence, Machine Learning and Data Assimilation have been massively applied to model, to anticipate, and to predict natural catastrophes, such as earthquakes, floods, landslides, volcanic eruptions, tsunamis, wildfires, glacier instabilities, in addition to multi-hazard and cascading effects, triggered by climate change. However, the adopted data-driven methods require a solid inductive bias, provided by the physics of the phenomenon at stake (or at least the understanding of it). Furthermore, due too often over-simplified assumptions, analytical models of natural catastrophes might encounter predictive limits. Therefore, several hybrid strategies, utilizing the ever increasing computational resources available, are currently being developed, to achieve more flexibility and full synergy between numerical physics-based simulations, machine learning and data-driven approaches.
The hybrid modelling of natural hazards benefits from the interpretability of numerical simulations and from the extrapolation and generalization capabilities of advanced Machine Learning methods. This synergy leads to multi-fidelity predictive tools that leverage all the available knowledge on the phenomenon at stake. Moreover, to tackle lack of data and representation, observational databases can be integrated with the synthetic results for re-analysis and for training machine learning algorithms on never-before-seen disaster scenarios.
This multidisciplinary session invites contributions addressing hybrid solutions to predict and to mitigate natural catastrophes (earthquakes, tsunamis, floods, wildfire, drought, hurricanes, hale etc.), blending high-performance computing, advanced numerical methods, reduced-order models, AI and data-driven statistical approaches, geospatial data analysis. The session welcomes both presentations on hybrid tools for hazard and vulnerability assessment (including environment-structure interaction).