activity
20242026
most citedRandONet: Shallow-Networks with Random Projections for learning linear and nonlinear operators

20 citations · 42 across the 14 of their papers we have counts for

collaborators

16 papers

math.NA2026

Autoencoders vs. Numerical Analysis--Informed Manifold Learning for Navier--Stokes Flows

Alessandro Della Pia, Lucia Russo, Ioannis Kevrekidis +1

Autoencoders (AEs) have become a dominant approach to nonlinear latent-space construction in data-driven reduced-order modelling (ROM), with their decoders lifting latent represent…

math.NA2026

Learning Contractive Integral Operators with Fredholm Integral Neural Operators

Kyriakos C. Georgiou, Constantinos Siettos, Athanasios N. Yannacopoulos

We generalize the framework of Fredholm Neural Networks, to learn non-expansive integral operators arising in Fredholm Integral Equations (FIEs) of the second kind in arbitrary dim…

math.NA2026

Stability and Bifurcation Analysis of Nonlinear PDEs via Random Projection-based PINNs: A Krylov-Arnoldi Approach

Gianluca Fabiani, Michail E. Kavousanakis, Constantinos Siettos +1

We present a numerical framework for the stability and bifurcation analysis of nonlinear partial differential equations (PDEs) in which the solutions are sought in the function spa…

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.NA2025

HEATNETs: Explainable Random Feature Neural Networks for High-Dimensional Parabolic PDEs

Kyriakos Georgiou, Gianluca Fabiani, Constantinos Siettos +1

We deal with the solution of the forward problem for high-dimensional parabolic PDEs with random feature (projection) neural networks (RFNNs). We first prove that there exists a si…

math.NA2025

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…