Alert button
Picture for Gianluigi Rozza

Gianluigi Rozza

Alert button

A Predictive Surrogate Model for Heat Transfer of an Impinging Jet on a Concave Surface

Add code
Bookmark button
Alert button
Feb 16, 2024
Sajad Salavatidezfouli, Saeid Rakhsha, Armin Sheidani, Giovanni Stabile, Gianluigi Rozza

Viaarxiv icon

Deep Reinforcement Learning for the Heat Transfer Control of Pulsating Impinging Jets

Add code
Bookmark button
Alert button
Sep 25, 2023
Sajad Salavatidezfouli, Giovanni Stabile, Gianluigi Rozza

Figure 1 for Deep Reinforcement Learning for the Heat Transfer Control of Pulsating Impinging Jets
Figure 2 for Deep Reinforcement Learning for the Heat Transfer Control of Pulsating Impinging Jets
Figure 3 for Deep Reinforcement Learning for the Heat Transfer Control of Pulsating Impinging Jets
Figure 4 for Deep Reinforcement Learning for the Heat Transfer Control of Pulsating Impinging Jets
Viaarxiv icon

Optimal Transport-inspired Deep Learning Framework for Slow-Decaying Problems: Exploiting Sinkhorn Loss and Wasserstein Kernel

Add code
Bookmark button
Alert button
Aug 26, 2023
Moaad Khamlich, Federico Pichi, Gianluigi Rozza

Figure 1 for Optimal Transport-inspired Deep Learning Framework for Slow-Decaying Problems: Exploiting Sinkhorn Loss and Wasserstein Kernel
Figure 2 for Optimal Transport-inspired Deep Learning Framework for Slow-Decaying Problems: Exploiting Sinkhorn Loss and Wasserstein Kernel
Figure 3 for Optimal Transport-inspired Deep Learning Framework for Slow-Decaying Problems: Exploiting Sinkhorn Loss and Wasserstein Kernel
Figure 4 for Optimal Transport-inspired Deep Learning Framework for Slow-Decaying Problems: Exploiting Sinkhorn Loss and Wasserstein Kernel
Viaarxiv icon

Generative Adversarial Reduced Order Modelling

Add code
Bookmark button
Alert button
May 25, 2023
Dario Coscia, Nicola Demo, Gianluigi Rozza

Figure 1 for Generative Adversarial Reduced Order Modelling
Figure 2 for Generative Adversarial Reduced Order Modelling
Figure 3 for Generative Adversarial Reduced Order Modelling
Figure 4 for Generative Adversarial Reduced Order Modelling
Viaarxiv icon

A DeepONet Multi-Fidelity Approach for Residual Learning in Reduced Order Modeling

Add code
Bookmark button
Alert button
Feb 24, 2023
Nicola Demo, Marco Tezzele, Gianluigi Rozza

Figure 1 for A DeepONet Multi-Fidelity Approach for Residual Learning in Reduced Order Modeling
Figure 2 for A DeepONet Multi-Fidelity Approach for Residual Learning in Reduced Order Modeling
Figure 3 for A DeepONet Multi-Fidelity Approach for Residual Learning in Reduced Order Modeling
Figure 4 for A DeepONet Multi-Fidelity Approach for Residual Learning in Reduced Order Modeling
Viaarxiv icon

A two stages Deep Learning Architecture for Model Reduction of Parametric Time-Dependent Problems

Add code
Bookmark button
Alert button
Jan 25, 2023
Isabella Carla Gonnella, Martin W. Hess, Giovanni Stabile, Gianluigi Rozza

Figure 1 for A two stages Deep Learning Architecture for Model Reduction of Parametric Time-Dependent Problems
Figure 2 for A two stages Deep Learning Architecture for Model Reduction of Parametric Time-Dependent Problems
Figure 3 for A two stages Deep Learning Architecture for Model Reduction of Parametric Time-Dependent Problems
Figure 4 for A two stages Deep Learning Architecture for Model Reduction of Parametric Time-Dependent Problems
Viaarxiv icon

Towards a machine learning pipeline in reduced order modelling for inverse problems: neural networks for boundary parametrization, dimensionality reduction and solution manifold approximation

Add code
Bookmark button
Alert button
Oct 26, 2022
Anna Ivagnes, Nicola Demo, Gianluigi Rozza

Figure 1 for Towards a machine learning pipeline in reduced order modelling for inverse problems: neural networks for boundary parametrization, dimensionality reduction and solution manifold approximation
Figure 2 for Towards a machine learning pipeline in reduced order modelling for inverse problems: neural networks for boundary parametrization, dimensionality reduction and solution manifold approximation
Figure 3 for Towards a machine learning pipeline in reduced order modelling for inverse problems: neural networks for boundary parametrization, dimensionality reduction and solution manifold approximation
Figure 4 for Towards a machine learning pipeline in reduced order modelling for inverse problems: neural networks for boundary parametrization, dimensionality reduction and solution manifold approximation
Viaarxiv icon

A Continuous Convolutional Trainable Filter for Modelling Unstructured Data

Add code
Bookmark button
Alert button
Oct 25, 2022
Dario Coscia, Laura Meneghetti, Nicola Demo, Giovanni Stabile, Gianluigi Rozza

Figure 1 for A Continuous Convolutional Trainable Filter for Modelling Unstructured Data
Figure 2 for A Continuous Convolutional Trainable Filter for Modelling Unstructured Data
Figure 3 for A Continuous Convolutional Trainable Filter for Modelling Unstructured Data
Figure 4 for A Continuous Convolutional Trainable Filter for Modelling Unstructured Data
Viaarxiv icon

A Proper Orthogonal Decomposition approach for parameters reduction of Single Shot Detector networks

Add code
Bookmark button
Alert button
Jul 27, 2022
Laura Meneghetti, Nicola Demo, Gianluigi Rozza

Figure 1 for A Proper Orthogonal Decomposition approach for parameters reduction of Single Shot Detector networks
Figure 2 for A Proper Orthogonal Decomposition approach for parameters reduction of Single Shot Detector networks
Figure 3 for A Proper Orthogonal Decomposition approach for parameters reduction of Single Shot Detector networks
Figure 4 for A Proper Orthogonal Decomposition approach for parameters reduction of Single Shot Detector networks
Viaarxiv icon

Non-linear manifold ROM with Convolutional Autoencoders and Reduced Over-Collocation method

Add code
Bookmark button
Alert button
Mar 01, 2022
Francesco Romor, Giovanni Stabile, Gianluigi Rozza

Figure 1 for Non-linear manifold ROM with Convolutional Autoencoders and Reduced Over-Collocation method
Figure 2 for Non-linear manifold ROM with Convolutional Autoencoders and Reduced Over-Collocation method
Figure 3 for Non-linear manifold ROM with Convolutional Autoencoders and Reduced Over-Collocation method
Figure 4 for Non-linear manifold ROM with Convolutional Autoencoders and Reduced Over-Collocation method
Viaarxiv icon