49 citations · 271 across the 22 of their papers we have counts for
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Enabling Local Neural Operators to perform Equation-Free System-Level Analysis
Gianluca Fabiani, Hannes Vandecasteele, Somdatta Goswami +2
Neural Operators (NOs) provide a powerful framework for computations involving physical laws that can be modelled by (integro-) partial differential equations (PDEs), directly lear…
RandONet: Shallow-Networks with Random Projections for learning linear and nonlinear operators
Gianluca Fabiani, Ioannis G. Kevrekidis, Constantinos Siettos +1
Deep Operator Networks (DeepOnets) have revolutionized the domain of scientific machine learning for the solution of the inverse problem for dynamical systems. However, their imple…
Random Projection Neural Networks of Best Approximation: Convergence theory and practical applications
Gianluca Fabiani
We investigate the concept of Best Approximation for Feedforward Neural Networks (FNN) and explore their convergence properties through the lens of Random Projection (RPNNs). RPNNs…
Tasks Makyth Models: Machine Learning Assisted Surrogates for Tipping Points
Gianluca Fabiani, Nikolaos Evangelou, Tianqi Cui +4
We present a machine learning (ML)-assisted framework bridging manifold learning, neural networks, Gaussian processes, and Equation-Free multiscale modeling, for (a) detecting tipp…
Discrete-Time Nonlinear Feedback Linearization via Physics-Informed Machine Learning
Hector Vargas Alvarez, Gianluca Fabiani, Nikolaos Kazantzis +2
We present a physics-informed machine learning (PIML) scheme for the feedback linearization of nonlinear discrete-time dynamical systems. The PIML finds the nonlinear transformatio…