collaborators

7 papers

physics.flu-dyn2026

Surrogate normal-forms for the numerical bifurcation and stability analysis of navier-stokes flows via machine learning

Alessandro Della Pia, Dimitrios G. Patsatzis, Gianluigi Rozza +2

Inspired by the Equation-Free paradigm, we propose an ``embed-learn-lift'' framework for constructing minimal-dimensional surrogate ROMs for the numerical analysis of high-fidelity…

math.NA2026

Invariant Manifolds of Discrete-time Dynamical Systems with Nonlinear Exosystems via Hybrid Physics-Informed Neural Networks

Dimitrios G. Patsatzis, Nikolaos Kazantzis, Ioannis G. Kevrekidis +2

We propose a hybrid physics-informed machine learning framework to approximate invariant manifolds (IMs) of discrete-time dynamical systems driven by exogenous autonomous dynamics…

cs.LG2026

Next Generation Equation-Free Multiscale Modelling of Crowd Dynamics via Machine Learning

Hector Vargas Alvarez, Dimitrios G. Patsatzis, Lucia Russo +2

Bridging the microscopic and macroscopic modelling scales in crowd dynamics constitutes an open challenge for systematic numerical analysis, optimization, and control. Here, we pro…

math.NA2026

RANDSMAPs: Random-Feature/multi-Scale Neural Decoders with Mass Preservation

Dimitrios G. Patsatzis, Alessandro Della Pia, Lucia Russo +1

We introduce RANDSMAPs (Random-feature/multi-scale neural decoders with Mass Preservation), numerical analysis-informed, explainable neural decoders designed to explicitly respect…

math.NA2024

GoRINNs: Godunov-Riemann Informed Neural Networks for Learning Hyperbolic Conservation Laws

Dimitrios G. Patsatzis, Mario di Bernardo, Lucia Russo +1

We present GoRINNs: numerical analysis-informed (shallow) neural networks for the solution of inverse problems of non-linear systems of conservation laws. GoRINNs is a hybrid/blend…

physics.flu-dyn2024

Learning the Latent dynamics of Fluid flows from High-Fidelity Numerical Simulations using Parsimonious Diffusion Maps

Alessandro Della Pia, Dimitris Patsatzis, Lucia Russo +1

We use parsimonious diffusion maps (PDMs) to discover the latent dynamics of high-fidelity Navier-Stokes simulations with a focus on the 2D fluidic pinball problem. By varying the…