Gianluca Fabiani is a PhD student in Modeling and Engineering Risk and Complexity (MERC) at Scuola Superiore Meridionale (SSM), Naples, Italy. Additionally, He have been recently a Visiting Student at the Department of Chemical and Biomolecular Engineering, Johns Hopkins University. Gianluca Fabiani holds a Bachelor’s Degree in Mathematics and a Master’s Degree in Applied Mathematics from the Department of Mathematics and Applications of the University of Naples Federico II. During his Master’s Degree, the direction of his studies has focused on the numerical analysis, machine learning, statistics and mathematical modeling of complex dynamical systems. He graduated with honours, conducting his MSc Thesis entitled “Learning Partial Differential Equations from Data with Machine Learning algorithms” under the supervision of Prof. Constantinos Siettos. His research interests have found a natural continuation within the PhD program in Modeling and Engineering Risk and Complexity (MERC) at the Shool of Advances Studies of Naples (SSM).
Ph.D. Student in Modelling and Engineering Risk and Complexity (MERC), at Scuola SUperiore Meridionale (SSM) (expected November 2024)
From ResearchGate and Google Scholar (Aggregate), updated up to March 25, 2024
Auricchio, F., Belardo, M. R., Calabrò, F., Fabiani, G. & Pascaner, A. F. (2024). On the accuracy of interpolation based on single-layer artificial neural networks. Soft Computing
Gnanadesikan, A., Fabiani, G., Liu, J., Gelderloos, R., Brett, G. J., Kevrekidis, Y., ... & Sleeman, J. (2023). Tipping points in overturning circulation mediated by ocean mixing and the configuration and magnitude of the hydrological cycle: A simple model. Journal of Physical Oceanography.
Patsatzis, D. G., Fabiani, G., Russo, L., & Siettos, C. (2024). Slow invariant manifolds of singularly perturbed systems via physics-informed machine learning. SIAM Journal on Scientific Computing, 2024
Fabiani, G., Evangelou, N., Cui, T., Bello-Rivas, J. M., Martin-Linares, C. P., Siettos, C., & Kevrekidis, I. G. (2023). Task-oriented Machine learning assisted surrogates for tipping points of agent-based models, Nature Communications, 2024
Hector Vargas Alvarez, Gianluca Fabiani, Nikolaos Kazantzis, Constantinos Siettos, Ioannis G. Kevrekidis, Discrete-time nonlinear feedback linearization via physics-informed machine learning, Journal of Computational Physics, Volume 492, 2023, 112408, ISSN 0021-9991, https://doi.org/10.1016/j.jcp.2023.112408.
Fabiani, G., Galaris, E., Russo, L., & Siettos, C. (2023). Parsimonious physics-informed random projection neural networks for initial value problems of ODEs and index-1 DAEs. Chaos: An Interdisciplinary Journal of Nonlinear Science, 33(4).
Galaris, E., Fabiani, G., Gallos, I., Kevrekidis, I., & Siettos, C. (2022). Numerical bifurcation analysis of pdes from lattice Boltzmann model simulations: a parsimonious machine learning approach. Journal of Scientific Computing, 92(2), 34.
Fabiani, G., Calabrò, F., Russo, L., & Siettos, C. (2021). Numerical solution and bifurcation analysis of nonlinear partial differential equations with extreme learning machines. Journal of Scientific Computing, 89, 1-35.
Calabrò, F., Fabiani, G., & Siettos, C. (2021). Extreme learning machine collocation for the numerical solution of elliptic PDEs with sharp gradients. Computer Methods in Applied Mechanics and Engineering, 387, 114188.
Alvarez, H. V., Fabiani, G., Kevrekidis, I. G., Kazantzis, N., & Siettos, C. (2024). Nonlinear Discrete-Time Observers with Physics-Informed Neural Networks. arXiv preprint arXiv:2402.12360.
Fabiani, G. (2024). Random Projection Neural Networks of Best Approximation: Convergence theory and practical applications. arXiv preprint arXiv:2402.11397.
Fabiani, G., Evangelou, N., Cui, T., Bello-Rivas, J. M., Martin-Linares, C. P., Siettos, C., & Kevrekidis, I. G. (2023). Tasks makyth models: Machine learning assisted surrogates for tipping points. arXiv preprint arXiv:2309.14334.
Patsatzis, D. G., Fabiani, G., Russo, L., & Siettos, C. (2023). Slow invariant manifolds of singularly perturbed systems via physics-informed machine learning. arXiv preprint arXiv:2309.07946.
Gnanadesikan, A., Fabiani, G., Liu, J., Gelderloos, R., Brett, G. J., Kevrekidis, Y., ... & Sleeman, J. (2023). Tipping points in overturning circulation mediated by ocean mixing and the configuration and magnitude of the hydrological cycle: A simple model. arXiv preprint arXiv:2308.03951.
Galaris, E., Fabiani, G., Calabrò, F., di Serafino, D., & Siettos, C. (2021). Numerical Solution of Stiff ODEs with Physics-Informed RPNNs. arXiv preprint arXiv:2108.01584.
Talk at SIAM UQ24, Trieste, Italy
Talk at CCS/Italy 2023, Naples, Italy
Talk at Dynamics Days Europe 2023, Naples, Italy
Talk at NUMTA DS23, Pizzo Calabro (VV), Italy
Talk at SIAM DS23, Portland, Oregon, USA
Talk at AAAI Spring Symposium Series, Palo Alto, California
Talk at 2022 Conference in Nonlinear Science and Complexity, Thessaloniki, Greece
Referee for the following international journals: