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Name (Acronym)Lab on AI-based Metamaterials & Metastructures Design (LAIM)
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ECTS2
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Contact E-mail2025.MAII.LAIM.UniTN.TRENTO.IT@eledia.org
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Teacher
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LanguageEnglish
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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 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.