M'AI: Agent’s PDEs 1000-1S25MAP
1. Agent-Based Models & Collective Dynamics
1.1 Vicsek, Cucker-Smale, and opinion dynamics
1.2 Phase transitions and self-organization
2. PDEs for Collective Systems
2.1 Mean-field limits: From particles to kinetic/Ginzburg-Landau-type PDEs
2.2 Nonlocal aggregation-diffusion equations (e.g., Keller-Segel)
3. Machine Learning Connections
3.1 Learning interaction kernels from data (neural networks)
3.2 Neural PDE solvers for agent-based systems
Type of course
Requirements
Prerequisites (description)
Course coordinators
Learning outcomes
The student knows examples of applications of partial differential equations in agent-based models and artificial intelligence. The student recognizes potential applications of artificial intelligence in constructing models through learning interactions. The student knows where they can further develop the knowledge acquired during the seminar.
Assessment criteria
Attendance and presentation
Additional information
Information on level of this course, year of study and semester when the course unit is delivered, types and amount of class hours - can be found in course structure diagrams of apropriate study programmes. This course is related to the following study programmes:
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