{"id":33,"date":"2026-02-08T11:53:42","date_gmt":"2026-02-08T11:53:42","guid":{"rendered":"https:\/\/www.dma.unina.it\/~NOA2MLprin2022\/?page_id=33"},"modified":"2026-03-08T13:06:37","modified_gmt":"2026-03-08T13:06:37","slug":"publications","status":"publish","type":"page","link":"https:\/\/www.dma.unina.it\/NOA2MLprin2022\/index.php\/publications\/","title":{"rendered":"Publications"},"content":{"rendered":"\t\t
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  • Ambrosio, P., Cuomo, S., De Rosa, M.,
    A physics-informed deep learning approach for solving strongly degenerate parabolic problems<\/em>,
    Engineering with Computers<\/strong>, 2025, 41(6), 4013-4029.
    DOI: 10.1007\/s00366-024-01961-9<\/h4><\/li>

  • Antonelli, L., De Simone, V., Viola, M.,
    A three-step framework for noisy image segmentation in brain MRI<\/em>,
    Applied Mathematics and Computation<\/strong>, 2026. 513
    DOI: 10.1016\/j.amc.2025.129803<\/h4><\/li>

  • Araboljadidi, N., De Simone, V.,
    Incremental Weak Subgradient Methods for Non-Smooth Non-Convex Optimization Problems<\/em>.
    Information<\/strong>, 2025, 16(6), 509.
    DOI: 10.3390\/info16060509<\/h4><\/li>

  • Bauduin, V., Cuomo, S., Schiano Di Cola, V.,
    Impact of collocation point sampling techniques on PINN performance in groundwater flow predictions<\/em>.
    Journal of Computational Mathematics and Data Science<\/strong>, 2025, 14, 100107.
    DOI: 10.1016\/j.jcmds.2024.100107 .<\/h4><\/li>

  • Bauduin, V., Cuomo, S.,\u00a0Schiano<\/span>\u00a0Di Cola, V. ,<\/span>
    <\/span>Constraint satisfaction approach in structuring neural network architectures<\/em>,
    <\/span>Journal of Computational and Applied Mathematics<\/strong>, 2026, 476, 117140.
    <\/span>DOI: 10.1016\/j.cam.2025.117140 .<\/span><\/h4><\/li>

  • Belardo, M.R., Calabr\u00f2, F.,Izzo, G., Messina, E., Veneroso, V.
    On the Construction of Nested Explicit Runge\u2013Kutta Methods via Null Rules<\/em>,
    Submitted, Available in SSRN<\/b>,
    DOI: 10.2139\/ssrn.5798402<\/h4><\/li>

  • Bellavia S., Kreji\u0107 N., Krklec Jerinki\u0107 N., Raydan M.,
    SLiSeS: subsampled line search spectral gradient method for finite sums<\/em>,
    Optimization Methods and Software<\/strong>, 2024, 1-26.
    DOI: https:\/\/doi.org\/10.1080\/10556788.2024.2426620<\/h4><\/li>

  • Bellavia S., B. Morini B., Rebegoldi S.,
    An investigation of stochastic trust-region based algorithms for finite-sum minimization<\/em>,
    Optimization Methods and Software<\/strong>, 2024, 39 (5).
    DOI: https:\/\/doi.org\/10.1080\/10556788.2024.2346834<\/h4><\/li>

  • Bellavia S., Palitta D., Porcelli M., Simoncini V.,
    Regularized methods via cubic subspace minimization for nonconvex optimization<\/em>,
    Computational Optimization and Applications<\/strong>, 2025 90.
    DOI: https:\/\/doi.org\/10.1007\/s10589-025-00655-2<\/h4><\/li>

  • Bellavia S., Malaspina G.,
    A discrete consensus-based global optimization method with noisy objective function<\/em>,
    Journal Optimization Theory and Applications<\/strong>, 2025, 206(20).
    DOI: https:\/\/doi.org\/10.1007\/s10957-025-02704-6<\/h4><\/li>

  • Bellavia S., Malaspina G., Morini B.,
    A variable sketching strategy for nonlinear least-squares,
    SIAM Journal of Scientific Computing<\/strong>, accepted for publication, 2025.<\/h4><\/li>

  • Bellavia S., Morini B., Yousefi M.,
    Fully stochastic trust-region methods with Barzilai-Borwein steplengths<\/em>,
    Journal of Computational and Applied Mathematics<\/strong>, 2026, 476, 117059.
    DOI: https:\/\/doi.org\/10.1016\/j.cam.2025.117059<\/h4><\/li>

  • Bellavia S., Malaspina G., Morini B.,
    Inexact Gauss-Newton methods with matrix approximation by sampling for nonlinear least-squares and systems<\/em>,
    Mathematics of Computation<\/strong>, 2026, 95(398).
    DOI: https:\/\/doi.org\/10.1090\/mcom\/4073<\/h4><\/li>

  • Benfenati, A., Catozzi, A., Franchini, G., Porta, F.,
    Early stopping strategies in Deep Image Prior<\/em>,
    Soft Computing<\/strong>, 2025, 29(8), pp. 4153\u20134174.
    DOI: 10.1007\/s00500-025-10642-8<\/h4><\/li>

  • Benfenati, A., Catozzi, A., Franchini, G., Porta, F.,
    Unsupervised noisy image segmentation using Deep Image Prior<\/em>,
    Mathematics and Computers in Simulation<\/strong>, 2026, 239, pp. 986\u20131003.
    DOI: 10.1016\/j.matcom.2025.07.052<\/h4><\/li>

  • Boscarino, S., Izzo, G.,
    High Order Semi-implicit Rosenbrock type and Multistep Methods for Evolutionary Partial Differential Equations with Higher Order Derivatives<\/em>, Submitted, ArXiv,
    DOI: 10.48550\/arXiv.2602.17507<\/h4><\/li>

  • Camellini, F., Crisci, S., De Magistris, A., Franchini , G.,
    A line-search based SGD algorithm with Adaptive Importance Sampling<\/em>,
    Journal of Computational and Applied Mathematics<\/strong>, 2025, 477, 117120.
    DOI: https:\/\/doi.org\/10.1016\/j.cam.2025.117120<\/h4><\/li>

  • Campagna, R., Crisci, S., Santin, G., Toraldo, G., Viola M.,
    An algorithm for a constrained P-spline<\/em>,
    BIT Numerical Mathematics<\/strong>, 2025, 65(2), 29.
    DOI: https:\/\/doi.org\/10.1007\/s10543-025-01071-y2.<\/h4><\/li>

  • Cascarano, P., Franchini, G., Kobler, E., Porta, F., Sebastiani, A.,
    A variable metric proximal stochastic gradient method: An application to classification problems<\/em>,
    Euro Journal on Computational Optimization<\/strong>, 2024, 12, 100088.
    DOI: 10.1016\/j.ejco.2024.100088<\/h4><\/li>

  • Corsaro S., De Simone V., Marino Z., Scognamiglio S.,
    Learning fused lasso parameters in portfolio selection via neural networks<\/em>,
    Quality and Quantity<\/strong>, 2024, 58, 4281\u20134299.
    DOI: 10.1007\/s11135-024-01858-1.<\/h4><\/li>

  • Crisci, S., De Simone, V., Pragliola, M., Toraldo, G.,
    Bilevel robust optimization approach for multi-period sparse portfolio selection<\/em>,
    Journal of Computational and Applied Mathematics<\/strong>, 2025, 470, 11672.
    DOI: 10.1016\/j.cam.2025.116729 1.<\/h4><\/li>

  • Crisci, S., Rebegoldi, S., Toraldo, G., Viola, M.,
    Barzilai-Borwein-like rules in proximal gradient schemes for l1-regularized problems<\/em>.
    Optimization Methods and Software<\/strong>, 2024, 9(3), pp. 601\u2013633.
    DOI:10.1080\/10556788.2023.2285489.<\/h4><\/li>

  • Cuomo, S., De Rosa, M., Piccialli, F., Pompameo, L., Vocca, V.,
    A numerical approach for soil microbiota growth prediction through physics-informed neural network<\/em>.
    Applied Numerical Mathematics<\/strong>, 2025, 207, 97-110.
    DOI: 10.1016\/j.apnum.2024.08.025 .<\/h4><\/li>

  • Cuomo, S., De Rosa, M., Piccialli, F., Pompameo, L.,
    Railway safety through predictive vertical displacement analysis using the pinn-ekf synergy<\/em>,
    Mathematics and Computers in Simulation<\/strong>, 2024, 223, 368-379.
    DOI: 10.1016\/j.matcom.2024.04.026 .<\/h4><\/li>

  • Cuomo, S., Gatta, F., Vocca, V.,
    Optimizing Liquidity Provision in Uniswap v3 via Physics-Informed Neural Networks<\/em>,
    Journal of Computational and Applied Mathematics<\/strong>, 2026, 117368.
    DOI: 10.1016\/j.cam.2026.117368 .<\/h4><\/li>

  • De Falco, D. E., Calabr\u00f2, F., Pragliola, M.,
    Insights on the different convergences in Extreme Learning Machine<\/em>,
    Neurocomputing<\/strong>, 2024, 599, 128061.
    DOI: 10.1016\/j.neucom.2024.128061 .<\/h4><\/li>

  • De Magistris, A., Crisci, S., De Simone, V., Toraldo, G.,
    A speed up strategy for gradient methods<\/em>.
    Computational Optimization and Applications<\/strong>, in press.<\/h4><\/li>

  • De Rosa, M., Pompameo, L., Litvinenko, A., Cuomo, S.,
    HOMO-PINN: Hyperparameter Optimization of a Multi-output Physics-Informed Neural Network<\/em>.
    Operations Research Forum<\/strong>, 2025 (Vol. 6, No. 4, p. 153). Cham: Springer International Publishing.
    DOI: 10.1007\/s43069-025-00561-7 .<\/h4><\/li>

  • Dell\u2019Amico, M., Franchini, G., Magnani, M., Zanni, L.,
    Can machine learning help in solving the pallet loading optimization problem?<\/em>
    Journal of Heuristics<\/strong>, 2026, 32(1), 11.
    DOI: 10.1007\/s10732-026-09586-5<\/h4><\/li>

  • Franchini, G., Porta, F., Ruggiero, V., Trombini, I., Zanni, L.,
    A stochastic gradient method with variance control and variable learning rate for Deep Learning<\/em>,
    Journal of Computational and Applied Mathematics<\/strong>, 2024, 451, 116083.
    DOI: 10.1016\/j.cam.2024.116083<\/h4><\/li>

  • Izzo, G., Jackiewicz, Z.,
    Self starting general linear methods with Runge\u2013Kutta stability<\/em>.
    Journal of Computational Dynamics<\/strong>, 2025, 12(1), 1-22.
    DOI: 10.3934\/jcd.2024023 .<\/h4><\/li>

  • Izzo, G., Messina, E., Pezzella, M., Vecchio, A.,
    Modified patankar linear multistep methods for production-destruction systems<\/em>.
    Journal of Scientific Computing<\/strong>, 2025, 102(3), 87.
    DOI: 10.1007\/s10915-025-02804-5 .<\/h4><\/li>

  • Jakovetic D., Krejic N, Malaspina G.,
    Distributed Inexact Newton Method with Adaptive Step Sizes<\/em>,
    Computational Optimization and Applications<\/strong>, 2025, 91.
    DOI: https:\/\/doi.org\/10.1007\/s10589-025-00666-z<\/h4><\/li>

  • Krklec Jerinki\u0107, N., Porta, F., Ruggiero, V., Trombini, I.,
    Variable metric proximal stochastic gradient methods with additional sampling<\/em>,
    Computational Optimization and Applications<\/strong>, 2026, 93(1), pp. 157-207.
    DOI: 10.1007\/s10589-025-00720-w<\/h4><\/li>

  • Lombardi, M., Sapienza, D., Govi, E., Franchini, G.,
    Hyperparameter optimization of an augmented autoencoder for 6D object pose estimation via neural architecture search<\/em>,
    Mathematics in Engineering<\/strong>, 2026, 8 (2), pp. 150-180.
    DOI: 10.3934\/mine.2026006<\/h4><\/li>

  • 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<\/em>
    2025 IEEE International Conference on Quantum Artificial Intelligence (QAI)<\/strong>, Naples, Italy, 2025, pp. 67-72,
    DOI: 10.1109\/QAI63978.2025.00018.<\/h4><\/li>

  • Morini B., Rebegoldi S.,
    Inexact restoration via random models for unconstrained noisy optimization<\/em>,
    Mathematics of Computation<\/strong>, accepted for publication, 2025.<\/h4><\/li>

  • 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<\/em>,
    Applied Soft Computing<\/strong>, 2025, 185, 113866.
    DOI: https:\/\/doi.org\/10.1016\/j.asoc.2025.113866<\/h4><\/li>

  • Nunziata, G., Coraggio, L., Crisci, S., De Gregorio, G., Neri, M., Roffilli, M., Schiattarella, R.,
    Quantum Fuzzy Edge Detection Algorithm: An Industrial Application<\/em>,
    2025 IEEE International Conference on Quantum Artificial Intelligence (QAI)<\/strong>, Naples, Italy, 2025, pp. 395-400,
    DOI: 10.1109\/QAI63978.2025.00067<\/h4><\/li>

  • 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<\/em>
    Applied Numerical Mathematics<\/strong>, 2025, 218, 58-72.
    DOI: 10.1016\/j.apnum.2025.06.015 .<\/h4><\/li>

  • Schiano Di Cola, V., Bauduin, V., Berardi, M., Notarnicola, F., Cuomo, S.,
    Investigating neural networks with groundwater flow equation loss<\/em>.
    Mathematics and Computers in Simulation<\/strong>, 2025, 230, 80-93.
    DOI: 10.1016\/j.matcom.2024.10.039 .<\/h4><\/li>

  • Schiassi, E., Calabr\u00f2, F. De Falco, D. E.,
    Pontryagin neural networks for the class of optimal control problems with integral quadratic cost<\/em>
    Aerospace Research Communications<\/strong>, 2024, 2, 13151.
    DOI: 10.1016\/j.jcp.2025.114553 .<\/h4><\/li>

  • Wu Z, Xie G, Ge Z., De Simone V.,
    Nonconvex multi-period mean-variance portfolio optimization<\/em>,
    Annals of Operations Research<\/strong>, 93, 303\u2013334., 2024.
    DOI: 10.1007\/s10479-023-05524-x)<\/h4><\/li>

  • Zini M., Bellavia S., Carcasci C., Rebegold S.,
    Tuning-free stochastic optimization for neural network training in building energy prediction<\/em>,
    Engineering Optimization<\/strong>, accepted for publication,
    DOI: https:\/\/doi.org\/10.1080\/0305215X.2025.2543279.<\/h4><\/li><\/ul>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"

    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, […]<\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"zakra_page_container_layout":"customizer","zakra_page_sidebar_layout":"customizer","zakra_remove_content_margin":false,"zakra_sidebar":"customizer","zakra_transparent_header":"customizer","zakra_logo":0,"zakra_main_header_style":"default","zakra_menu_item_color":"","zakra_menu_item_hover_color":"","zakra_menu_item_active_color":"","zakra_menu_active_style":"","zakra_page_header":true,"footnotes":""},"class_list":["post-33","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/www.dma.unina.it\/NOA2MLprin2022\/index.php\/wp-json\/wp\/v2\/pages\/33","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.dma.unina.it\/NOA2MLprin2022\/index.php\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/www.dma.unina.it\/NOA2MLprin2022\/index.php\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/www.dma.unina.it\/NOA2MLprin2022\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.dma.unina.it\/NOA2MLprin2022\/index.php\/wp-json\/wp\/v2\/comments?post=33"}],"version-history":[{"count":16,"href":"https:\/\/www.dma.unina.it\/NOA2MLprin2022\/index.php\/wp-json\/wp\/v2\/pages\/33\/revisions"}],"predecessor-version":[{"id":162,"href":"https:\/\/www.dma.unina.it\/NOA2MLprin2022\/index.php\/wp-json\/wp\/v2\/pages\/33\/revisions\/162"}],"wp:attachment":[{"href":"https:\/\/www.dma.unina.it\/NOA2MLprin2022\/index.php\/wp-json\/wp\/v2\/media?parent=33"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}