Research

Research Vision

My research focuses on algorithmic metamodeling for complex systems. I develop mathematical and computational frameworks to represent, predict, and prescriptively orient complex dynamics under uncertainty, partial observability, and structural constraints.

My work combines applied mathematics, system identification, scientific machine learning, structure-preserving modeling, and predictive and prescriptive analytics, with applications to industrial, financial, and networked cyber-physical-human systems (CPHS).

A current direction of this program is the development of stable step strategies: algorithmic metamodels for sustainable cooperation in networked cyber-physical-human systems, aimed at identifying how distributed systems can preserve functionality, redistribute resources, and maintain coherent behavior under uncertainty.

Research Interests

Algorithmic metamodeling for complex systems
Structure-preserving function approximation and modeling
System identification and scientific machine learning
Reservoir computing and neural representations of dynamical systems
Koopman-based spectral methods and pseudospectral analysis
Dynamical systems on networks
Model reduction and surrogate modeling
Optimization, control, and prescriptive analytics under uncertainty
Networked cyber-physical-human systems

Selected Publications

Structured Reservoir Computing, System Identification, and CPHS

L. Banegas, F. Vides, Stochastically Structured Reservoir Computers for Financial and Economic System Identification, IFAC-PapersOnLine Elsevier, Volume 59, Issue 36, 2025, Pages 100-105. https://doi.org/10.1016/j.ifacol.2026.03.018

F. Vides, E. Segura, C. Vargas-Agüero. A Subspace Method for Time Series Anomaly Detection in Cyber-Physical Systems. IFAC-PapersOnLine Elsevier, Volume 55, Issue 41, 2022, Pages 58-63. https://doi.org/10.1016/j.ifacol.2023.01.103

F. Vides. Computing Semilinear Sparse Models for Approximately Eventually Periodic Signals. IFAC-PapersOnLine Elsevier, 2022. http://dx.doi.org/10.1016/j.ifacol.2022.09.096

F. Vides. On Cyclic Finite-State Approximation of Data-Driven Systems. Proceedings CONCAPAN XXXIX. IEEE Xplore, 2019. PDF

Optimization, Control, and Surrogate Modeling

C. Menezes Rebello, E. Almeida Costa, A. Santos Sánchez, F. Vides, I. B. R. Nogueira. Assuring optimality in surrogate-based optimization: A novel theorem and its practical implementation in pressure swing adsorption optimization. Canadian Journal of Chemical Engineering, 2025, 103(3), 1036. https://doi.org/10.1002/cjce.25512

A. Nazerian, F. Vides, F. Sorrentino. Algebraic Decomposition of Model Predictive Control Problems. IEEE Control Systems Letters, vol. 7, pp. 1441-1446, 2023. https://doi.org/10.1109/LCSYS.2023.3252162

T. A. Loring, F. Vides. Computing Truncated Joint Approximate Eigenbases for Model Order Reduction. In Discussion Contributions, 10th Vienna Conference on Mathematical Modelling, ARGESIM, 2022, Vol. 17.

Structure-Preserving Matrix Analysis and Operator-Theoretic Methods

A. Cerjan, T. A. Loring, F. Vides. Quadratic pseudospectrum for identifying localized states. Journal of Mathematical Physics 64, 023501, 2023. https://doi.org/10.1063/5.0098336

T. A. Loring, F. Vides. Computing Floquet Hamiltonians with Symmetries. Journal of Mathematical Physics 61, 113501, 2020. https://doi.org/10.1063/5.0023028

F. Vides. On Uniform Connectivity of Algebraic Matrix Sets. Banach Journal of Mathematical Analysis, Vol. 13, No. 4, pp. 918-943, 2019. PDF

T. A. Loring, F. Vides. Local Matrix Homotopies and Soft Tori. Banach Journal of Mathematical Analysis 12, no. 1, 167-190, 2018. PDF

T. A. Loring, F. Vides. Estimating Norms of Commutators. Experimental Mathematics, 24(1):106-122, 2015. PDF