7 Febbraio, 2025 11:00

Material point method for compressible flows: a portable, massively parallel implementation for HPC architectures

Paolo Joseph Baioni, Politecnico di Milano

Aula Saleri - VI piano

7 Febbraio, 2025 11:00

Particle-based numerical methods for landslides: run-out and impact analysis

Marco Fois, Politecnico di Milano

Aula Saleri - VI piano

7 Febbraio, 2025 15:00

Enhancing numerical methods through deep learning

Matteo Caldana, Politecnico di Milano

Aula Saleri - VI piano

7 Febbraio, 2025 15:00

Mathematical models and numerical methods for neurodegenerative diseases

Mattia Corti, Politecnico di Milano

Aula Saleri - VI piano

7 Febbraio, 2025 15:00

Numerical methods for epidemic forecast and control

Giovanni Ziarelli, Politecnico di Milano

Aula Saleri - VI piano

6 Febbraio, 2025 14:30

Flow and mechanics in fractured porous media: from high fidelity models to efficient reduced order solutions

Enrico Ballini, Politecnico di Milano

Aula Saleri - VI piano

6 Febbraio, 2025 14:30

Numerical methods for simulation and optimization of wave energy converter arrays

Marco Gambarini, Politecnico di Milano

Aula Saleri - VI piano

6 Febbraio, 2025 14:30

Advanced multiphysics computational models for the vascular microenvironment

Piermario Vitullo, Politecnico di Milano

Aula Saleri - VI piano

4 Febbraio, 2025 14:00

MOX Seminar

Splines vs. Neural Networks: How Novel Machine Learning Approaches Influence Design Optimization

Stefanie Elgeti, ILSB - Vienna University of Technology, CATS - RWTH Aachen

Aula Saleri

abstract

abstract

Product innovation is a multi-step process: a creative phase where ideas are born, an evaluation phase where the ideas are evaluated, and an implementation phase where these ideas become tangible. While computer-based assistance systems are already available for the latter two phases, creativity is often still considered an exclusively human attribute. However, recent advances in artificial intelligence (AI) have challenged this notion, as creative AI agents are increasingly integrated into our daily lives and have demonstrated their potential to create original content (e.g., ChatGPT, DALL-E, MuseNet, DeepDream). In light of these advances, a new field of research has emerged in the area of AI-enabled design processes, leading to a more-than-human design process in which a computer agent collaborates with a design team to efficiently and creatively explore the entire design space in search of novel design solutions.
To this end, we will demonstrate new technologies, such as how Variational Autoencoders (VAE) can be used to learn low-dimensional, yet feature-rich shape representations. This approach promises significant improvements in both performance and variety of shapes that can be learned. The resulting geometric representation is then incorporated into a shape optimization framework. In addition, we explore the potential of reinforcement learning (RL) as an optimization strategy. RL is based on the trial-and-error interaction of an agent with its environment. As such, RL can be characterized as experience-driven, autonomous learning. While not necessarily superior to classical optimization algorithms (such as gradient-based approaches) for a single optimization problem, based on the existing literature, we expect RL techniques to thrive when recurrent optimization tasks arise.

Contatto:
nicola.parolini@polimi.it


This initiative is part of the “Ph.D. Lectures” activity of the project "Departments of Excellence 2023-2027" of the Department of Mathematics of Politecnico di Milano. This activity consists of seminars open to Ph.D. students, followed by meetings with the speaker to discuss and go into detail on the topics presented at the talk. 

4 Febbraio, 2025 11:00

MOX Seminar

Bayesian nonparametric analysis of spatial and spatio-temporal data: modelling and computation

Matteo Gianella, Politecnico di Milano

Aula Saleri - VI piano

Product innovation is a multi-step process: a creative phase where ideas are born, an evaluation phase where the ideas are evaluated, and an implementation phase where these ideas become tangible. While computer-based assistance systems are already available for the latter two phases, creativity is often still considered an exclusively human attribute. However, recent advances in artificial intelligence (AI) have challenged this notion, as creative AI agents are increasingly integrated into our daily lives and have demonstrated their potential to create original content (e.g., ChatGPT, DALL-E, MuseNet, DeepDream). In light of these advances, a new field of research has emerged in the area of AI-enabled design processes, leading to a more-than-human design process in which a computer agent collaborates with a design team to efficiently and creatively explore the entire design space in search of novel design solutions.
To this end, we will demonstrate new technologies, such as how Variational Autoencoders (VAE) can be used to learn low-dimensional, yet feature-rich shape representations. This approach promises significant improvements in both performance and variety of shapes that can be learned. The resulting geometric representation is then incorporated into a shape optimization framework. In addition, we explore the potential of reinforcement learning (RL) as an optimization strategy. RL is based on the trial-and-error interaction of an agent with its environment. As such, RL can be characterized as experience-driven, autonomous learning. While not necessarily superior to classical optimization algorithms (such as gradient-based approaches) for a single optimization problem, based on the existing literature, we expect RL techniques to thrive when recurrent optimization tasks arise.

Contatto:
nicola.parolini@polimi.it


This initiative is part of the “Ph.D. Lectures” activity of the project "Departments of Excellence 2023-2027" of the Department of Mathematics of Politecnico di Milano. This activity consists of seminars open to Ph.D. students, followed by meetings with the speaker to discuss and go into detail on the topics presented at the talk.