Il prossimo appuntamento con la serie di incontri PhDTalks si terrà Martedì 4 febbraio nell’aula Fassò (Edificio 4A), dalle 17:15 alle 18:30 CET.

PhDTalks è una serie di seminari e discussioni tra dottorandi. Gli eventi hanno lo scopo di fornire un luogo dove creare un network tra dottorandi ed entrare in contatto con i molti progetti sviluppati nel nostro dipartimento.

Lo speaker José Pablo Quesada Molina condurrà un seminario dal titolo “Accelerating Materials Design with Deep Learning: Multiscale Modeling and Inverse Design Strategies”. 

Al termine dell’evento sarà disponibile un piccolo rinfresco finanziato dal dipartimento.

Sarà possibile seguire la conferenza anche online al seguente link.

Abstract

This talk delves into the transformative role of deep learning in advancing multiscale modeling and inverse design of heterogeneous materials, with a focus on predictive and generative data-driven approaches. Two case studies exemplify this potential: the first examines polysilicon films commonly used in microelectromechanical systems, employing Convolutional Neural Networks to develop surrogate models that reliably link microstructural characteristics to macroscopic elastic properties. The second explores an inverse design framework for composite materials using Deep Convolutional Generative Adversarial Networks, enabling accelerated material discovery and optimization. These studies demonstrate how deep learning enhances material property prediction and design, emphasizing the interplay between data availability and model generalization.

Speaker’s bio

José Pablo completed his Bachelor’s degree in Mechanical Engineering at Universidad de Costa Rica, where he earned a scholarship to pursue postgraduate studies at Politecnico di Milano, Italy. In July 2020, he graduated with a Master’s degree in Materials Engineering and Nanotechnology. Shortly after, in November 2020, he began his Ph.D. studies in the Structural Seismic and Geotechnical Engineering program.
His research focuses on applying deep learning (DL) strategies to tackle challenges in the modeling and design of microstructurally heterogeneous materials, such as polycrystalline solids and composites. By utilizing advanced architectures like Convolutional Neural Networks and Generative Adversarial Networks, his work aims to accelerate material discovery, optimize structural responses, and predict effective properties across multiple scales. Additionally, his research investigates the generalization capabilities of data-driven models in complex systems, with a particular emphasis on exploring the potential of DL to effectively address out-of-distribution scenarios
José Pablo’s Ph.D. studies are funded by Universidad de Costa Rica as part of a program dedicated to enhancing the academic qualifications of its teaching staff. After completing his Ph.D., he will return to Costa Rica as a full-time professor in the Mechanical Engineering Department, where he plans to contribute to academic excellence and foster research in computational materials science.

In his free time, José Pablo enjoys hiking, exploring nature, and reading.