By bridging the gap between traditional physical understanding and modern data-driven approaches, participants will gain unique competencies in surrogate modelling and structural optimisation.

For who?

PhD student and MSc student

Course Highlights:

·         Physics-AI Integration: Learn to combine physics-based modelling with AI-driven approaches for complex dynamic systems.

·         Lattice Metamaterials: Explore the design of lattice-based materials for superior energy absorption using AI-assisted methods.

·         Hands-on Validation: Participate in making sessions that include rapid prototyping and 3D printing of structural designs.

·         International Collaboration: Engage in a challenge-based learning environment with peers and experts from Politecnico di Milano and NTNU

Schedule:

•              Online part (common to all participants): April 7, 9, 14, 16 — from 2:00 PM to 4:00 PM

•              Core course at Polimi: April 20–24 — from 9:00 AM to 6:00 PM

•              Core course at NTNU: June 23–24 — from 9:00 AM to 4:00 PM

During the on-site sessions at Polimi and NTNU, activities will be accessible also online.

Practical Details:

·         Credits: 4 ECTS. The students will also receive an ENHANCE certificate of achievement upon completion

·         Format: Hybrid

·         Entry Requirements: Basic knowledge of structural engineering and introductory programming skills in MATLAB or similar languages are required.

·         Before the in-person sessions, online learning materials will be made available and short live sessions will be held online to provide fundamental concepts on numerical simulations, machine learning techniques, and dynamic loading conditions, in order to align the background level of all participants.

·          Commitment: Students must attend both the Milan and Trondheim phases (in-person or online), with a maximum allowable absence of 30%. Evaluation is based on active participation (20%), a team challenge involving a technical presentation and report (50%), and a final scientific reflection (30%)

Apply now -  https://forms.gle/KyauPDpLxKqQF5qH8

Applications are open from 19 February until 12 March at 12:00 PM

More information:  General info_Deep Learning–Driven Design under Dynamic Loads