Granular materials exhibit complex, nonsmooth, and multiscale behaviours that pose significant challenges to current predictive models and computing resources. Yet, these materials are central to a wide spectrum of industrial and environmental processes. The inherent complex rheology makes their handling, processing and use highly challenging and energy consuming. They also play a key role in understanding and mitigating natural hazards like landslides and avalanches, whose impacts have been intensifying due to climate change.
AIM’s interdisciplinary methodology bridges applied mathematics, mechanics, and artificial intelligence to better understand, model, and predict the mechanics and dynamics of granular media. By combining high-fidelity particle-scale simulations, cutting-edge in-operando experiments, and developing AI methods constrained by the fundamental principles from statistical physics and non-equilibrium thermodynamics, AIM will deliver a proof of principle to robustly and accurately predict the fine- and large-scale behaviour of granular systems.
The project will also establish an innovation consortium dedicated to open-source software and build an interdisciplinary training program at the intersection of AI and mechanics, equipping the next generation of scientists and engineers.
SELECTED LIST OF PUBLICATIONS
F. Masi, I. Einav (2024). Neural integration for constitutive equations using small data. Computer Methods in Applied Mechanics and Engineering, 420, 116698.
F. Masi, I. Stefanou (2022). Multiscale modeling of inelastic materials with Thermodynamics-based Artificial Neural Networks (TANN). Computer Methods in Applied Mechanics and Engineering, 398, 115190.
M. Arbel, J. Mairal (2022). Non-convex bilevel games with critical point selection maps. Advances in Neural Information Processing Systems, 582, 8013-8026.
F. Masi, I. Stefanou, P. Vannucci, V. Maffi-Berthier (2021). Thermodynamics-based artificial neural networks for constitutive modeling. Journal of the Mechanics and Physics of Solids, 147, 104277.
M. Bińkowski, D.J. Sutherland, M. Arbel, A. Gretton (2018). Demystifying MMD GANs. International Conference on Learning Representations.
F. Dubois, V. Acary, M. Jean (2018). The Contact Dynamics method: A nonsmooth story. Comptes Rendus Mécanique, 346(3), 247-262.
V. Acary (2013). Projected event-capturing time-stepping schemes for nonsmooth mechanical systems with unilateral contact and Coulomb’s friction. Computer Methods in Applied Mechanics and Engineering, 256, 224-250.
V. Acary, F. Pérignon (2007). Siconos: A software platform for modeling, simulation, analysis and control of nonsmooth dynamical systems. SNE Simulation News Europe, 17(3/4), 19-26.
CHAIR PRESENTATION
License:
Unless otherwise stated, all documents are shared under the Creative Commons BY-NC-ND 4.0 license.
You may view and share them for non-commercial purposes, without modification, and with appropriate credit to the authors.
Published on August 21, 2025 Updated on August 21, 2025
Share the linkCopyCopiedClose the modal windowShare the URL of this pageI recommend:Consultable at this address:La page sera alors accessible depuis votre menu "Mes favoris".Stop videoPlay videoMutePlay audioChat: A question? Chatbot Robo FabricaMatomo traffic statisticsX (formerly Twitter)
Vincent Acary
