Filippo Gatti - Generative strategies to empower physics-based wave propagation with deep learning. Applications to earthquake engineering.

Nous aurons l'occasion d'écouter une présentation de Filippo Gatti (OMEIR, Laboratoire de Mécanique de Paris Saclay) intitulée Generative strategies to empower physics-based wave propagation with deep learning. Applications to earthquake engineering. le 3 avril 2025 à 14h à l'ENSTA Paris en Amphi 2234.

Abstract:
In this work, we provide a quantitative assessment of how largely can earthquake groundmotion simulation benefit from deep learning generative techniques, blended with traditional numerical simulations. Two main frameworks are addressed: conditional generative approaches and neural operators. On one hand, a diffusions model is employed in a time-series super-resolution context. The main task is to improve the outcome of 3D fault-to-site earthquake numerical simulations (accurate up to 5 Hz [1, 2]) at higher frequencies (5-30 Hz), by learning the low-to-high frequency mapping from seismograms recorded worldwide [3, 4]. The generation is conditioned by the numerical simulation synthetic time-histories, enabling fast inference for site-specific probabilistic hazard assessment.  Finally, the successful use of neural operators to entirely replace cumbersome 3D elastic wave propagation numerical simulations is described [5,6], showing how this approach can pave the way to real-time large-scale digital twins of earthquake prone regions [6,7].

Bibliography:
[1] Touhami, S.; Gatti, F.; Lopez-Caballero, F.; Cottereau, R.; de Abreu Corrêa, L.; Aubry, L.; Clouteau, D. SEM3D: A 3D High-Fidelity Numerical Earthquake Simulator for Broadband (0–10 Hz) Seismic Response Prediction at a Regional Scale. Geosciences 2022, 12 (3), 112. https://doi.org/10.3390/geosciences12030112.
[2] Gatti, F.; Carvalho Paludo, L. D.; Svay, A.; Lopez-Caballero, F.-; Cottereau, R.; Clouteau, D. Investigation of the Earthquake Ground Motion Coherence in Heterogeneous Non-Linear Soil Deposits. Procedia Engineering 2017, 199, 2354–2359.
https://doi.org/10.1016/j.proeng.2017.09.232.
[3] Gatti, F.; Clouteau, D. Towards Blending Physics-Based Numerical Simulations and Seismic Databases Using Generative Adversarial Network. Computer Methods in Applied Mechanics and Engineering 2020, 372, 113421. https://doi.org/10.1016/j.cma.2020.113421.
[4] Gabrielidis, H.; Gatti, F.; Vialle, S.; Jacquet, G. Génération conditionnelle et inconditionnelle de signaux sismiques à l’aide de modèles de diffusion. In 16ème Colloque National en Calcul des Structures; Computational Structural Mechanics Association: Giens, 2024; pp 1-9. https://hal.science/hal-04531795.
[5] Lehmann, F.; Gatti, F.; Bertin, M.; Clouteau, D. 3D Elastic Wave Propagation with a Factorized Fourier Neural Operator (F-FNO). Computer Methods in Applied Mechanics and Engineering 2024, 420, 116718. https://doi.org/10.1016/j.cma.2023.116718.
[6] Lehmann, F.; Gatti, F.; Clouteau, D. Multiple-Input Fourier Neural Operator (MIFNO) for Source-Dependent 3D Elastodynamics. Journal of Computational Physics 2025, 527, 113813. https://doi.org/10.1016/j.jcp.2025.113813.
[7] Lehmann, F.; Gatti, F.; Bertin, M.; Clouteau, D. Machine Learning Opportunities to Conduct High-Fidelity Earthquake Simulations in Multi-Scale Heterogeneous Geology. Front. Earth Sci. 2022, 10, 1029160. https://doi.org/10.3389/feart.2022.1029160.