1 citations · 1 across the 7 of their papers we have counts for
8 papers
AURA: Angular Update Rate Adaptation for training complex-valued neural networks
Enrico Ballini, Allan Peter Engsig-Karup, Tito Andriollo
Complex-valued neural networks (CVNNs) are increasingly adopted for complex-valued data; however, they are often trained with first-order optimizers inherited from the real-valued…
A holomorphic neural network framework for 3D boundary value problems governed by harmonic potentials
Enrico Ballini, Allan Peter Engsig-Karup, Tito Andriollo
We present a neural-network-based framework for the solution of three-dimensional boundary value problems where the solution is expressible in terms of harmonic potentials. The app…
Elimination-compensation pruning for fully-connected neural networks
Enrico Ballini, Luca Muscarnera, Alessio Fumagalli +2
The unmatched ability of Deep Neural Networks in capturing complex patterns in large and noisy datasets is often associated with their large hypothesis space, and consequently to t…
Model reduction of parametric ordinary differential equations via autoencoders: representation properties and convergence analysis
Enrico Ballini, Marco Gambarini, Alessio Fumagalli +3
We propose a reduced-order modeling approach for nonlinear, parameter-dependent ordinary differential equations (ODE). Dimensionality reduction is achieved using nonlinear maps rep…
Active Flow Control for Bluff Body under High Reynolds Number Turbulent Flow Conditions Using Deep Reinforcement Learning
Jingbo Chen, Enrico Ballini, Stefano Micheletti
This study employs Deep Reinforcement Learning (DRL) for active flow control in a turbulent flow field of high Reynolds numbers at . That is, an agent is trained to obta…
A hybrid upwind scheme for two-phase flow in fractured porous media
Enrico Ballini, Luca Formaggia, Alessio Fumagalli +2
Simulating the flow of two fluid phases in porous media is a challenging task, especially when fractures are included in the simulation. Fractures may have highly heterogeneous pro…
Application of Deep Learning Reduced-Order Modeling for Single-Phase Flow in Faulted Porous Media
Enrico Ballini, Luca Formaggia, Alessio Fumagalli +2
We apply reduced-order modeling (ROM) techniques to single-phase flow in faulted porous media, accounting for changing rock properties and fault geometry variations using a radial…
Reducing the Drag of a Bluff Body by Deep Reinforcement Learning
Enrico Ballini, Alberto Silvio Chiappa, Stefano Micheletti
We present a deep reinforcement learning approach to a classical problem in fluid dynamics, i.e., the reduction of the drag of a bluff body. We cast the problem as a discrete-time…