Laboratory on AI-based Metamaterials & Metastructures Design

Name (Acronym)

Laboratory on AI-based Metamaterials & Metastructures Design (LAIM)
Date

06 November – 07 November 2025
(15 hours)
Teacher(s)

BROCCARDO Marco, SALUCCI Marco
Contact E-mail

2025.MAII.LAIM.UniTN.TRENTO.IT@eledia.org
ECTS

2
Syllabus

ESSE3 Page
Institution

M3 Postgraduate Program, University of Trento
Study Program

Postgraduate Program Multifunctional Metamaterials and Metastructures [MAII M3]
Degree

Postgraduate
Language

English
Tracks

Artificial Intelligence, Wave Manipulations and Artificial/Smart Materials

ABSTRACT

The design of high-performance metamaterials and metastructures is a highly challenging problem from both the methodological and computational viewpoints due to the intrinsic complexity inherited from their multi-scale nature. Indeed, it often involves thousands of degrees-of-freedoms even for moderate-size devices composed by rather “simple” unit cells (UCs). Artificial Intelligence (AI) is a powerful tool to develop accurate and efficient surrogate models for predicting the response of a system as a function of both its material/geometric descriptors and the external excitation. This forms the foundation for developing efficient “digital twins” (DTs). The laboratory aims at providing the methodological skills and knowledge on efficient AI-driven design of metamaterials and metastructures through computer-guided examples.

COURSE FORMAT

The Course is taught in 🇬🇧️ ENGLISH and offered

On-site
On-line (synchronous and asynchronous)

with video recordings, hand-outs, etc. of the lectures available off-line (*).

COURSE CONTENT

Fundamentals of AI and Machine Learning (ML) algorithms
ML as a "three-steps" process for building accurate DTs
Dimensionality reduction and single-shot/adaptive sampling techniques
Surrogate model for computational expensive metastructures and metamaterials
System-by-Design (SbD) framework for the computationally-efficient design of complex multi-scale structures
Applicative examples of AI-based metamaterials and metastructures designs

TEACHING ACTIVITIES

Theoretical Lessons
e-Xam Self Assessment (each teaching class or periodically)
SW/HW Emulator Exercises
e-Xam Final Assessment

FURTHER READINGS

A. I. J. Forrester, A. Sobester, and A. J. Keane, Engineering Design via Surrogate Modelling: A Practical Guide. Hoboken, N.J.: John Wiley & Sons, 2008.
T. Hastie, J. Friedman, and R. Tisbshirani, The Elements of Statistical Learning: Data Mining, Inference, and Prediction. New York: Springer, 2017.
V. N. Vapnik, The Nature of Statistical Learning Theory. New York: Springer, 2000.
M. Z. Naser, Machine Learning for Civil & Environmental Engineers. Wiley, 2023.
M. Li and M. Salucci, Eds., Applications of Deep Learning in Electromagnetics - Teaching Maxwell's Equations to Machines. London, United Kingdom: IET, 2022.

For further references please contact the Teacher(s).

(*) Each registered participant acknowledges that the material distributed in the frame of the course, available for the duration of one academic year, is protected by copyright and delivered for educational purposes and personal use only. The participant agrees and undertakes not to forward, publish, disclose, distribute, disseminate - in any form or manner - such a material without written consent of the author(s) of the material. Unless otherwise explicitly allowed by the speaker in written form, no recordings of the online lectures can be made.