6 papers
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…
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…
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…
PDE-Free Mass-Constrained Learning of Complex Systems with Hidden States
Gianmaria Viola, Alessandro Della Pia, Lucia Russo +2
We propose a three-tier machine learning framework based on the next-generation Equation-Free algorithm for learning the spatio-temporal dynamics of mass-constrained complex system…
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…
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…