Ambrosio, P., Cuomo, S., De Rosa, M., A physics-informed deep learning approach for solving strongly degenerate parabolic problems, Engineering with Computers, 2025, 41(6), 4013-4029. DOI: 10.1007/s00366-024-01961-9
Antonelli, L., De Simone, V., Viola, M., A three-step framework for noisy image segmentation in brain MRI, Applied Mathematics and Computation, 2026. 513 DOI: 10.1016/j.amc.2025.129803
Araboljadidi, N., De Simone, V., Incremental Weak Subgradient Methods for Non-Smooth Non-Convex Optimization Problems. Information, 2025, 16(6), 509. DOI: 10.3390/info16060509
Bauduin, V., Cuomo, S., Schiano Di Cola, V., Impact of collocation point sampling techniques on PINN performance in groundwater flow predictions. Journal of Computational Mathematics and Data Science, 2025, 14, 100107. DOI: 10.1016/j.jcmds.2024.100107 .
Bauduin, V., Cuomo, S., Schiano Di Cola, V. , Constraint satisfaction approach in structuring neural network architectures, Journal of Computational and Applied Mathematics, 2026, 476, 117140. DOI: 10.1016/j.cam.2025.117140 .
Belardo, M.R., Calabrò, F.,Izzo, G., Messina, E., Veneroso, V. On the Construction of Nested Explicit Runge–Kutta Methods via Null Rules, Submitted, Available in SSRN, DOI: 10.2139/ssrn.5798402
Bellavia S., Krejić N., Krklec Jerinkić N., Raydan M., SLiSeS: subsampled line search spectral gradient method for finite sums, Optimization Methods and Software, 2024, 1-26. DOI: https://doi.org/10.1080/10556788.2024.2426620
Bellavia S., B. Morini B., Rebegoldi S., An investigation of stochastic trust-region based algorithms for finite-sum minimization, Optimization Methods and Software, 2024, 39 (5). DOI: https://doi.org/10.1080/10556788.2024.2346834
Bellavia S., Palitta D., Porcelli M., Simoncini V., Regularized methods via cubic subspace minimization for nonconvex optimization, Computational Optimization and Applications, 2025 90. DOI: https://doi.org/10.1007/s10589-025-00655-2
Bellavia S., Malaspina G., A discrete consensus-based global optimization method with noisy objective function, Journal Optimization Theory and Applications, 2025, 206(20). DOI: https://doi.org/10.1007/s10957-025-02704-6
Bellavia S., Malaspina G., Morini B., A variable sketching strategy for nonlinear least-squares, SIAM Journal of Scientific Computing, accepted for publication, 2025.
Bellavia S., Morini B., Yousefi M., Fully stochastic trust-region methods with Barzilai-Borwein steplengths, Journal of Computational and Applied Mathematics, 2026, 476, 117059. DOI: https://doi.org/10.1016/j.cam.2025.117059
Bellavia S., Malaspina G., Morini B., Inexact Gauss-Newton methods with matrix approximation by sampling for nonlinear least-squares and systems, Mathematics of Computation, 2026, 95(398). DOI: https://doi.org/10.1090/mcom/4073
Benfenati, A., Catozzi, A., Franchini, G., Porta, F., Early stopping strategies in Deep Image Prior, Soft Computing, 2025, 29(8), pp. 4153–4174. DOI: 10.1007/s00500-025-10642-8
Benfenati, A., Catozzi, A., Franchini, G., Porta, F., Unsupervised noisy image segmentation using Deep Image Prior, Mathematics and Computers in Simulation, 2026, 239, pp. 986–1003. DOI: 10.1016/j.matcom.2025.07.052
Boscarino, S., Izzo, G., High Order Semi-implicit Rosenbrock type and Multistep Methods for Evolutionary Partial Differential Equations with Higher Order Derivatives, Submitted, ArXiv, DOI: 10.48550/arXiv.2602.17507
Camellini, F., Crisci, S., De Magistris, A., Franchini , G., A line-search based SGD algorithm with Adaptive Importance Sampling, Journal of Computational and Applied Mathematics, 2025, 477, 117120. DOI: https://doi.org/10.1016/j.cam.2025.117120
Campagna, R., Crisci, S., Santin, G., Toraldo, G., Viola M., An algorithm for a constrained P-spline, BIT Numerical Mathematics, 2025, 65(2), 29. DOI: https://doi.org/10.1007/s10543-025-01071-y2.
Cascarano, P., Franchini, G., Kobler, E., Porta, F., Sebastiani, A., A variable metric proximal stochastic gradient method: An application to classification problems, Euro Journal on Computational Optimization, 2024, 12, 100088. DOI: 10.1016/j.ejco.2024.100088
Corsaro S., De Simone V., Marino Z., Scognamiglio S., Learning fused lasso parameters in portfolio selection via neural networks, Quality and Quantity, 2024, 58, 4281–4299. DOI: 10.1007/s11135-024-01858-1.
Crisci, S., De Simone, V., Pragliola, M., Toraldo, G., Bilevel robust optimization approach for multi-period sparse portfolio selection, Journal of Computational and Applied Mathematics, 2025, 470, 11672. DOI: 10.1016/j.cam.2025.116729 1.
Crisci, S., Rebegoldi, S., Toraldo, G., Viola, M., Barzilai-Borwein-like rules in proximal gradient schemes for l1-regularized problems. Optimization Methods and Software, 2024, 9(3), pp. 601–633. DOI:10.1080/10556788.2023.2285489.
Cuomo, S., De Rosa, M., Piccialli, F., Pompameo, L., Vocca, V., A numerical approach for soil microbiota growth prediction through physics-informed neural network. Applied Numerical Mathematics, 2025, 207, 97-110. DOI: 10.1016/j.apnum.2024.08.025 .
Cuomo, S., De Rosa, M., Piccialli, F., Pompameo, L., Railway safety through predictive vertical displacement analysis using the pinn-ekf synergy, Mathematics and Computers in Simulation, 2024, 223, 368-379. DOI: 10.1016/j.matcom.2024.04.026 .
Cuomo, S., Gatta, F., Vocca, V., Optimizing Liquidity Provision in Uniswap v3 via Physics-Informed Neural Networks, Journal of Computational and Applied Mathematics, 2026, 117368. DOI: 10.1016/j.cam.2026.117368 .
De Falco, D. E., Calabrò, F., Pragliola, M., Insights on the different convergences in Extreme Learning Machine, Neurocomputing, 2024, 599, 128061. DOI: 10.1016/j.neucom.2024.128061 .
De Magistris, A., Crisci, S., De Simone, V., Toraldo, G., A speed up strategy for gradient methods. Computational Optimization and Applications, in press.
De Rosa, M., Pompameo, L., Litvinenko, A., Cuomo, S., HOMO-PINN: Hyperparameter Optimization of a Multi-output Physics-Informed Neural Network. Operations Research Forum, 2025 (Vol. 6, No. 4, p. 153). Cham: Springer International Publishing. DOI: 10.1007/s43069-025-00561-7 .
Dell’Amico, M., Franchini, G., Magnani, M., Zanni, L., Can machine learning help in solving the pallet loading optimization problem? Journal of Heuristics, 2026, 32(1), 11. DOI: 10.1007/s10732-026-09586-5
Franchini, G., Porta, F., Ruggiero, V., Trombini, I., Zanni, L., A stochastic gradient method with variance control and variable learning rate for Deep Learning, Journal of Computational and Applied Mathematics, 2024, 451, 116083. DOI: 10.1016/j.cam.2024.116083
Izzo, G., Jackiewicz, Z., Self starting general linear methods with Runge–Kutta stability. Journal of Computational Dynamics, 2025, 12(1), 1-22. DOI: 10.3934/jcd.2024023 .
Izzo, G., Messina, E., Pezzella, M., Vecchio, A., Modified patankar linear multistep methods for production-destruction systems. Journal of Scientific Computing, 2025, 102(3), 87. DOI: 10.1007/s10915-025-02804-5 .
Jakovetic D., Krejic N, Malaspina G., Distributed Inexact Newton Method with Adaptive Step Sizes, Computational Optimization and Applications, 2025, 91. DOI: https://doi.org/10.1007/s10589-025-00666-z
Krklec Jerinkić, N., Porta, F., Ruggiero, V., Trombini, I., Variable metric proximal stochastic gradient methods with additional sampling, Computational Optimization and Applications, 2026, 93(1), pp. 157-207. DOI: 10.1007/s10589-025-00720-w
Lombardi, M., Sapienza, D., Govi, E., Franchini, G., Hyperparameter optimization of an augmented autoencoder for 6D object pose estimation via neural architecture search, Mathematics in Engineering, 2026, 8 (2), pp. 150-180. DOI: 10.3934/mine.2026006
Massa, A. Nunziata, G., Polverino, F., Campanile, L., Castaldo, M., Coraggio, L., Crisci, S., De Gregorio, G., and Ibnsalih, W. I., and Itaco, N., Landolfi, E., Marrone, S., Toraldo, G., Troiano, A., A Quantum Machine Learning Algorithm for Hazelnut Variety Recognition 2025 IEEE International Conference on Quantum Artificial Intelligence (QAI), Naples, Italy, 2025, pp. 67-72, DOI: 10.1109/QAI63978.2025.00018.
Morini B., Rebegoldi S., Inexact restoration via random models for unconstrained noisy optimization, Mathematics of Computation, accepted for publication, 2025.
Nunziata, G., Crisci, S., De Gregorio, G., Schiattarella, R., Acampora, G., Coraggio, L., Itaco N., Quantum fuzzy logic for edge detection: A demonstration on NISQ hardware, Applied Soft Computing, 2025, 185, 113866. DOI: https://doi.org/10.1016/j.asoc.2025.113866
Nunziata, G., Coraggio, L., Crisci, S., De Gregorio, G., Neri, M., Roffilli, M., Schiattarella, R., Quantum Fuzzy Edge Detection Algorithm: An Industrial Application, 2025 IEEE International Conference on Quantum Artificial Intelligence (QAI), Naples, Italy, 2025, pp. 395-400, DOI: 10.1109/QAI63978.2025.00067
Schiano Di Cola, V., Cuomo, S., Severino, G., Berardi, M., Algebraic multigrid methods for uncertainty quantification of source-type flows through randomly heterogeneous porous media Applied Numerical Mathematics, 2025, 218, 58-72. DOI: 10.1016/j.apnum.2025.06.015 .
Schiano Di Cola, V., Bauduin, V., Berardi, M., Notarnicola, F., Cuomo, S., Investigating neural networks with groundwater flow equation loss. Mathematics and Computers in Simulation, 2025, 230, 80-93. DOI: 10.1016/j.matcom.2024.10.039 .
Schiassi, E., Calabrò, F. De Falco, D. E., Pontryagin neural networks for the class of optimal control problems with integral quadratic cost Aerospace Research Communications, 2024, 2, 13151. DOI: 10.1016/j.jcp.2025.114553 .
Wu Z, Xie G, Ge Z., De Simone V., Nonconvex multi-period mean-variance portfolio optimization, Annals of Operations Research, 93, 303–334., 2024. DOI: 10.1007/s10479-023-05524-x)
Zini M., Bellavia S., Carcasci C., Rebegold S., Tuning-free stochastic optimization for neural network training in building energy prediction, Engineering Optimization, accepted for publication, DOI: https://doi.org/10.1080/0305215X.2025.2543279.