8 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…
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…