The course is Free of Charge
Overview
Are you working with large-scale data? Developing complex models? Or simulating real-world systems?
This hands-on workshop is designed to help researchers at CINECA accelerate their AI workflows using MATLAB, Simulink, and Simscape – fully integrated with CINECA’s HPC infrastructure.
Over two interactive sessions, you’ll:
- Build deep learning models using real-world inspired data
- Run and scale your training on the HPC cluster
- Integrate AI into simulations of physical systems
- Tackle a guided challenge project, working alongside facilitators to apply everything you've learned
Whether you're in computational science, engineering, life sciences, or system modeling, this workshop will equip you with scalable AI techniques you can bring into your own research – from prototyping to deployment.
Key Takeaways
- Build and train deep learning models using MATLAB (low-code + scripted workflows)
- Submit and scale training jobs on CINECA's HPC
- Use Simulink to simulate intelligent systems with embedded AI
- Apply AI to simulate and analyze physical systems using Simscape
- Deploy trained models to edge devices, embedded systems, or cloud platforms
- Work on a practical challenge project, inspired by real-world applications
- Learn best practices for reproducibility, performance, and deployment
Session Highlights
DAY 1:
Session 1 – Build & Scale AI: (JUL 8th 14.30-16.30)
Train a model using MATLAB’s Deep Learning Toolbox and scale it across HPC nodes. Understand where AI fits into your scientific workflows.
Session 2 – Apply AI in Simulation & Systems: (JUL 9th 10.30-12.30)
Work with Simulink and Simscape to integrate your trained model into a dynamic system. Collaborate on a challenge project simulating a real-world scenario in your field – with support from the facilitators.
Who Should Attend
Researchers, engineers, and domain experts who are:
- Using HPC for simulation, modeling, or data-heavy research
- Exploring how to apply AI in their domain
- Interested in integrating deep learning into their physical system models or simulations