{"id":151,"date":"2025-06-28T19:33:27","date_gmt":"2025-06-28T17:33:27","guid":{"rendered":"https:\/\/project.inria.fr\/aimechanics\/?page_id=151"},"modified":"2025-06-30T17:48:56","modified_gmt":"2025-06-30T15:48:56","slug":"about-aim","status":"publish","type":"page","link":"https:\/\/project.inria.fr\/aimechanics\/about-aim\/","title":{"rendered":"About AIM"},"content":{"rendered":"\n

Granular materials exhibit complex, nonsmooth, and multiscale behaviors 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.<\/p>\n\n\n\n

AIM’s interdisciplinary methodology bridges applied mathematics, mechanics, and artificial intelligence to better understand, model, and predict the mechanics and dynamics of granular media.<\/strong>
By combining high-fidelity particle-scale simulations, cutting-edge in-operando experiments, and AI methods constrained with 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 behavior of granular systems.<\/p>\n\n\n\n

\"\"<\/figure><\/div>\n\n\n\n

AIM’s research is conducted at the Universit\u00e9 Grenoble Alpes (UGA) and the National Institute for Research in Digital Science and Technology (Inria<\/a>). AIM’s research members are based at TRIPOP<\/a> and THOTH<\/a> teams, INRAe-IGE<\/a>, and 3SR<\/a>.<\/p>\n\n\n\n

\"\"<\/a><\/figure><\/div>\n\n\n\n
\n

AIM is supported by the Multidisciplinary Institute in Artificial Intelligence | MIAI Cluster AI and the Agence Nationale de la Recherche through the France 2030 program (Grant agreement ANR-23-IACL-0006)<\/p>\n<\/div><\/div>\n\n\n\n

\"\"<\/a><\/figure><\/div>\n","protected":false},"excerpt":{"rendered":"

Granular materials exhibit complex, nonsmooth, and multiscale behaviors 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\u2026<\/p>\n

Continue reading<\/span><\/i><\/a> <\/p>\n","protected":false},"author":2580,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-151","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/project.inria.fr\/aimechanics\/wp-json\/wp\/v2\/pages\/151","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/project.inria.fr\/aimechanics\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/project.inria.fr\/aimechanics\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/project.inria.fr\/aimechanics\/wp-json\/wp\/v2\/users\/2580"}],"replies":[{"embeddable":true,"href":"https:\/\/project.inria.fr\/aimechanics\/wp-json\/wp\/v2\/comments?post=151"}],"version-history":[{"count":21,"href":"https:\/\/project.inria.fr\/aimechanics\/wp-json\/wp\/v2\/pages\/151\/revisions"}],"predecessor-version":[{"id":332,"href":"https:\/\/project.inria.fr\/aimechanics\/wp-json\/wp\/v2\/pages\/151\/revisions\/332"}],"wp:attachment":[{"href":"https:\/\/project.inria.fr\/aimechanics\/wp-json\/wp\/v2\/media?parent=151"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}