57 citations · 618 across the 33 of their papers we have counts for
33 papers
Trainable Spline Representations for Physics-Informed Learning
Giovanni Canali, Nicola Demo, Gianluigi Rozza
This work introduces Physics-Informed Splines (PI-Splines), a structured spline-based architecture for physics-informed learning. Instead of representing the solution of a differen…
Constraint-driven Optimization and Parametrization of Industrial NURBS Geometries via Neural Deformation Field
Federico Tamburlin, Giovanni Canali, Giuseppe Alessio D'Inverno +3
This work presents a differentiable framework for the parametrization and shape optimization of industrial CAD geometries represented by multi-patch NURBS surfaces. The method enab…
Machine Learning-based quadratic closures for non-intrusive Reduced Order Models
Gabriele Codega, Anna Ivagnes, Nicola Demo +1
In the present work, we introduce a data-driven approach to enhance the accuracy of non-intrusive Reduced Order Models (ROMs). In particular, we focus on ROMs built using Proper Or…
BARNN: A Bayesian Autoregressive and Recurrent Neural Network
Dario Coscia, Max Welling, Nicola Demo +1
Autoregressive and recurrent networks have achieved remarkable progress across various fields, from weather forecasting to molecular generation and Large Language Models. Despite t…
Data-driven Discovery of Delay Differential Equations with Discrete Delays
Alessandro Pecile, Nicola Demo, Marco Tezzele +2
The Sparse Identification of Nonlinear Dynamics (SINDy) framework is a robust method for identifying governing equations, successfully applied to ordinary, partial, and stochastic…
Non-intrusive model reduction of advection-dominated hyperbolic problems using neural network shift augmented manifold transformation
Harshith Gowrachari, Nicola Demo, Giovanni Stabile +1
Advection-dominated problems are predominantly noticed in nature, engineering systems, and various industrial processes. Traditional linear compression methods, such as proper orth…
Kinetic data-driven approach to turbulence subgrid modeling
Giulio Ortali, Alessandro Gabbana, Nicola Demo +2
Numerical simulations of turbulent flows are well known to pose extreme computational challenges due to the huge number of dynamical degrees of freedom required to correctly descri…
PyDMD: A Python package for robust dynamic mode decomposition
Sara M. Ichinaga, Francesco Andreuzzi, Nicola Demo +5
The dynamic mode decomposition (DMD) is a simple and powerful data-driven modeling technique that is capable of revealing coherent spatiotemporal patterns from data. The method's l…
Generative Adversarial Reduced Order Modelling
Dario Coscia, Nicola Demo, Gianluigi Rozza
In this work, we present GAROM, a new approach for reduced order modelling (ROM) based on generative adversarial networks (GANs). GANs have the potential to learn data distribution…
A Shape Optimization Pipeline for Marine Propellers by means of Reduced Order Modeling Techniques
Anna Ivagnes, Nicola Demo, Gianluigi Rozza
In this paper, we propose a shape optimization pipeline for propeller blades, applied to naval applications. The geometrical features of a blade are exploited to parametrize it, al…
A Graph-based Framework for Complex System Simulating and Diagnosis with Automatic Reconfiguration
Martina Teruzzi, Nicola Demo, Gianluigi Rozza
Fault detection has a long tradition: the necessity to provide the most accurate diagnosis possible for a process plant criticality is somehow intrinsic in its functioning. Continu…
A DeepONet multi-fidelity approach for residual learning in reduced order modeling
Nicola Demo, Marco Tezzele, Gianluigi Rozza
In the present work, we introduce a novel approach to enhance the precision of reduced order models by exploiting a multi-fidelity perspective and DeepONets. Reduced models provide…
Towards a machine learning pipeline in reduced order modelling for inverse problems: neural networks for boundary parametrization, dimensionality reduction and solution manifold approximation
Anna Ivagnes, Nicola Demo, Gianluigi Rozza
In this work, we propose a model order reduction framework to deal with inverse problems in a non-intrusive setting. Inverse problems, especially in a partial differential equation…
A Continuous Convolutional Trainable Filter for Modelling Unstructured Data
Dario Coscia, Laura Meneghetti, Nicola Demo +2
Convolutional Neural Network (CNN) is one of the most important architectures in deep learning. The fundamental building block of a CNN is a trainable filter, represented as a disc…
A Proper Orthogonal Decomposition approach for parameters reduction of Single Shot Detector networks
Laura Meneghetti, Nicola Demo, Gianluigi Rozza
As a major breakthrough in artificial intelligence and deep learning, Convolutional Neural Networks have achieved an impressive success in solving many problems in several fields i…
A Dimensionality Reduction Approach for Convolutional Neural Networks
Laura Meneghetti, Nicola Demo, Gianluigi Rozza
The focus of this paper is the application of classical model order reduction techniques, such as Active Subspaces and Proper Orthogonal Decomposition, to Deep Neural Networks. We…
An extended physics informed neural network for preliminary analysis of parametric optimal control problems
Nicola Demo, Maria Strazzullo, Gianluigi Rozza
In this work we propose an extension of physics informed supervised learning strategies to parametric partial differential equations. Indeed, even if the latter are indisputably us…
A Dynamic Mode Decomposition Extension for the Forecasting of Parametric Dynamical Systems
Francesco Andreuzzi, Nicola Demo, Gianluigi Rozza
Dynamic mode decomposition (DMD) has recently become a popular tool for the non-intrusive analysis of dynamical systems. Exploiting Proper Orthogonal Decomposition (POD) as a dimen…
The Neural Network shifted-Proper Orthogonal Decomposition: a Machine Learning Approach for Non-linear Reduction of Hyperbolic Equations
Davide Papapicco, Nicola Demo, Michele Girfoglio +2
Models with dominant advection always posed a difficult challenge for projection-based reduced order modelling. Many methodologies that have recently been proposed are based on the…
Hull shape design optimization with parameter space and model reductions, and self-learning mesh morphing
Nicola Demo, Marco Tezzele, Andrea Mola +1
In the field of parametric partial differential equations, shape optimization represents a challenging problem due to the required computational resources. In this contribution, a…
Gaussian process approach within a data-driven POD framework for fluid dynamics engineering problems
Giulio Ortali, Nicola Demo, Gianluigi Rozza
This work describes the implementation of a data-driven approach for the reduction of the complexity of parametrical partial differential equations (PDEs) employing Proper Orthogon…
A supervised learning approach involving active subspaces for an efficient genetic algorithm in high-dimensional optimization problems
Nicola Demo, Marco Tezzele, Gianluigi Rozza
In this work, we present an extension of the genetic algorithm (GA) which exploits the supervised learning technique called active subspaces (AS) to evolve the individuals on a low…
An efficient computational framework for naval shape design and optimization problems by means of data-driven reduced order modeling techniques
Nicola Demo, Giulio Ortali, Gianluca Gustin +2
This contribution describes the implementation of a data--driven shape optimization pipeline in a naval architecture application. We adopt reduced order models (ROMs) in order to i…
Enhancing CFD predictions in shape design problems by model and parameter space reduction
Marco Tezzele, Nicola Demo, Giovanni Stabile +2
In this work we present an advanced computational pipeline for the approximation and prediction of the lift coefficient of a parametrized airfoil profile. The non-intrusive reduced…