{"id":5263,"date":"2026-02-27T18:07:31","date_gmt":"2026-02-27T18:07:31","guid":{"rendered":"https:\/\/e-nns.org\/icann2026\/?page_id=5263"},"modified":"2026-03-10T09:20:18","modified_gmt":"2026-03-10T09:20:18","slug":"from-real-systems-to-ai-solutions-learning-for-spatial-temporal-dynamics","status":"publish","type":"page","link":"https:\/\/e-nns.org\/icann2026\/from-real-systems-to-ai-solutions-learning-for-spatial-temporal-dynamics\/","title":{"rendered":"From real systems to AI solutions: Learning for spatial temporal dynamics"},"content":{"rendered":"\n

Real-world spatiotemporal systems\u2014such as urban transportation, maritime, and low-altitude aviation economies\u2014generate large, heterogeneous, and continuously evolving data streams with strong spatial dependencies, long-range temporal dynamics, and significant operational constraints. While recent advances in AI-based modeling have shown promising results on benchmark datasets, many approaches remain insufficiently grounded in real operational settings. This half-day workshop focuses on AI-enabled solutions for real-world spatiotemporal systems, with particular emphasis on transportation, maritime networks, and low-altitude aviation economies. The workshop brings together researchers and leading domain experts with deep system-level knowledge to present real operational challenges, data limitations, and decision-making constraints through invited keynote and expert talks. Building on these real-system perspectives, the workshop explores how modern learning methods\u2014such as deep neural models, graph and sequence representations, state-space approaches, and constraint-aware modeling\u2014can be designed to meaningfully resolve practical spatiotemporal problems, supporting robust prediction and decision-making in complex systems.<\/p>\n\n\n\n

Workshop Organizers<\/h2>\n\n\n\n