Publications

Below we provide a list of publications that use RBniCS.

Is your publication missing? Contact us by email to have your publication added to the list.

Year 2024

  1. G. Agarwal, J.-H. Urrea-Quintero, H. Wessels, and T. Wick. Parameter identification and uncertainty propagation of hydrogel coupled diffusion-deformation using pod-based reduced-order modeling. Submitted, 2024. arXiv:2403.08968.

  2. S. Ali, F. Ballarin, and G. Rozza. An online stabilization method for parametrized viscous flows. In G. Rozza, G. Stabile, M. Gunzburger, and M. D'Elia, editors, Reduction, Approximation, Machine Learning, Surrogates, Emulators and Simulators: RAMSES, pages 1–16. Springer Nature Switzerland, 2024. doi:10.1007/978-3-031-55060-7_1.

  3. J. Genovese, F. Ballarin, G. Rozza, and C. Canuto. Weighted reduced order methods for uncertainty quantification in computational fluid dynamics. In G. Rozza, G. Stabile, M. Gunzburger, and M. D'Elia, editors, Reduction, Approximation, Machine Learning, Surrogates, Emulators and Simulators: RAMSES, pages 127–151. Springer Nature Switzerland, 2024. arXiv:2303.14432, doi:10.1007/978-3-031-55060-7_6.

  4. G. Padula, M. Girfoglio, and G. Rozza. A brief review of reduced order models using intrusive and non-intrusive techniques. Submitted, 2024. arXiv:2406.00559.

  5. S. Rathore, P. C. Africa, F. Ballarin, F. Pichi, M. Girfoglio, and G. Rozza. Projection-based reduced order modelling for unsteady parametrized optimal control problems in 3d cardiovascular flows. Submitted, 2024. arXiv:2410.20828.

  6. G. Rozza, F. Ballarin, L. Scandurra, and F. Pichi. Real Time Reduced Order Computational Mechanics. SISSA Springer Series. Springer Cham, 2024. ISBN 978-3-031-49891-6.

Year 2023

  1. F. Ballarin, S. Lee, and S.-Y. Yi. Projection-based reduced order modeling of an iterative coupling scheme for thermo-poroelasticity. Submitted, 2023. arXiv:2309.01004.

  2. T. Kadeethum, J. D. Jakeman, Y. Choi, N. Bouklas, and H. Yoon. Epistemic uncertainty-aware barlow twins reduced order modeling for nonlinear contact problems. IEEE Access, 11:62970–62985, 2023. doi:10.1109/ACCESS.2023.3284837.

  3. M. Nonino, F. Ballarin, G. Rozza, and Y. Maday. A reduced basis method by means of transport maps for a fluid–structure interaction problem with slowly decaying Kolmogorov n-width. Advances in Computational Science and Engineering, 1(1):36–58, 2023. doi:10.3934/acse.2023002.

  4. M. Nonino, F. Ballarin, G. Rozza, and Y. Maday. Projection based semi-implicit partitioned reduced basis method for fluid–structure interaction problems. Journal of Scientific Computing, 94(1):4, 2023. arXiv:2201.03236, doi:10.1007/s10915-022-02049-6.

  5. F. Pichi, F. Ballarin, G. Rozza, and J. S. Hesthaven. An artificial neural network approach to bifurcating phenomena in computational fluid dynamics. Computers & Fluids, 254:105813, 2023. doi:10.1016/j.compfluid.2023.105813.

  6. F. Pichi, B. Moya, and J. S. Hesthaven. A graph convolutional autoencoder approach to model order reduction for parametrized pdes. Submitted, 2023. arXiv:2305.08573.

  7. I. Prusak. Application of optimisation-based domain–decomposition reduced order models to parameter-dependent fluid dynamics and multiphysics problems. PhD thesis, Mathematical Analysis, Modelling, and Applications, SISSA, Italy, Dec. 2023. URL: https://hdl.handle.net/20.500.11767/135830.

  8. I. Prusak, M. Nonino, D. Torlo, and G. Rozza. An optimisation-based domain-decomposition reduced order model for parameter-dependent non-stationary fluid dynamics problems. Submitted, 2023. arXiv:2308.01733.

  9. M. Strazzullo, F. Ballarin, T. Iliescu, and C. Canuto. New feedback control and adaptive evolve-filter-relax regularization for the Navier-Stokes equations in the convection-dominated regime. Submitted, 2023. arXiv:2307.00675.

  10. F. Zoccolan, M. Strazzullo, and G. Rozza. A streamline upwind petrov-galerkin reduced order method for advection-dominated partial differential equations under optimal control. Submitted, 2023. arXiv:2301.01973.

  11. F. Zoccolan, M. Strazzullo, and G. Rozza. Stabilized weighted reduced order methods for parametrized advection-dominated optimal control problems governed by partial differential equations with random inputs. Submitted, 2023. arXiv:2301.01975.

Year 2022

  1. F. Ballarin, G. Rozza, and M. Strazzullo. Space-time POD-Galerkin approach for parametric flow control. In E. Trélat and E. Zuazua, editors, Numerical Control: Part A, volume 23 of Handbook of Numerical Analysis, pages 307–338. Elsevier, 2022. doi:10.1016/bs.hna.2021.12.009.

  2. T. Kadeethum, F. Ballarin, Y. Choi, D. O'Malley, H. Yoon, and N. Bouklas. Non-intrusive reduced order modeling of natural convection in porous media using convolutional autoencoders: comparison with linear subspace techniques. Advances in Water Resources, 160:104098, 2022. doi:10.1016/j.advwatres.2021.104098.

  3. M. Khamlich, F. Pichi, and G. Rozza. Model order reduction for bifurcating phenomena in fluid-structure interaction problems. International Journal for Numerical Methods in Fluids, 94(10):1611–1640, 2022. doi:10.1002/fld.5118.

  4. F. Pichi, M. Strazzullo, F. Ballarin, and G. Rozza. Driving bifurcating parametrized nonlinear PDEs by optimal control strategies: application to Navier-Stokes equations with model order reduction. ESAIM: Mathematical Modelling and Numerical Analysis, 56(4):1361–1400, 2022. doi:10.1051/m2an/2022044.

  5. I. Prusak, M. Nonino, D. Torlo, F. Ballarin, and G. Rozza. An optimisation-based domain-decomposition reduced order model for the incompressible Navier-Stokes equations. Submitted, 2022. arXiv:2211.14528.

  6. G. Rozza, G. Stabile, and F. Ballarin. Advanced Reduced Order Methods and Applications in Computational Fluid Dynamics. Computational science and engineering. Society for Industrial and Applied Mathematics, Philadelphia, PA, 2022. ISBN 978-1-611977-24-0. doi:10.1137/1.9781611977257.

  7. N. V. Shah. Coupled parameterized reduced order modelling of thermomechanical phenomena arising in blast furnaces. PhD thesis, Mathematical Analysis, Modelling, and Applications, SISSA, Italy, Sep. 2022. URL: https://hdl.handle.net/20.500.11767/127929.

  8. N. V. Shah, M. Girfoglio, P. Quintela, G. Rozza, A. Lengomin, F. Ballarin, and P. Barral. Finite element based model order reduction for parametrized one-way coupled steady state linear thermo-mechanical problems. Finite Elements in Analysis and Design, 212:103837, 2022. doi:10.1016/j.finel.2022.103837.

  9. M. Strazzullo, F. Ballarin, and G. Rozza. POD-Galerkin model order reduction for parametrized nonlinear time-dependent optimal flow control: an application to shallow water equations. Journal of Numerical Mathematics, 3(1):63–84, 2022. doi:10.1515/jnma-2020-0098.

  10. M. Strazzullo, M. Girfoglio, F. Ballarin, T. Iliescu, and G. Rozza. Consistency of the full and reduced order models for evolve-filter-relax regularization of convection-dominated, marginally-resolved flows. International Journal for Numerical Methods in Engineering, 123(14):3148–3178, 2022. doi:10.1002/nme.6942.

  11. M. Strazzullo and F. Vicini. POD-based reduced order methods for optimal control problems governed by parametric partial differential equation with varying boundary control. Submitted, 2022. arXiv:2212.10654.

Year 2021

  1. S. Ali, F. Ballarin, and G. Rozza. A reduced basis stabilization for the unsteady stokes and navier-stokes equations. Submitted, 2021. arXiv:2103.03553.

  2. G. Carere, M. Strazzullo, F. Ballarin, G. Rozza, and R. Stevenson. A weighted pod-reduction approach for parametrized pde-constrained optimal control problems with random inputs and applications to environmental sciences. Computers & Mathematics with Applications, 102:261–276, 2021. doi:10.1016/j.camwa.2021.10.020.

  3. A. Gorodetsky, J. D. Jakeman, and G. Geraci. MFNets: data efficient all-at-once learning of multifidelity surrogates as directed networks of information sources. Computational Mechanics, 68:741–758, 2021. doi:10.1007/s00466-021-02042-0.

  4. T. Kadeethum, F. Ballarin, and N. Bouklas. Data-driven reduced order modeling of poroelasticity of heterogeneous media based on a discontinuous galerkin approximation. GEM - International Journal on Geomathematics, 12:12, 2021. doi:10.1007/s13137-021-00180-4.

  5. E. N. Karatzas, M. Nonino, F. Ballarin, and G. Rozza. A reduced order cut finite element method for geometrically parametrized steady and unsteady navier-stokes problems. Computers & Mathematics with Applications, 2021. doi:10.1016/j.camwa.2021.07.016.

  6. E. N. Karatzas and G. Rozza. A reduced order model for a stable embedded boundary parametrized Cahn-Hilliard phase-field system based on cut finite elements. Journal of Scientific Computing, 89:9, 2021. doi:10.1007/s10915-021-01623-8.

  7. S. H. Le, S. Kang, T. M. Pham, and K. Lee. Novel geometric parameterization scheme for the certified reduced basis analysis of a square unit cell. Journal of the Korean Society for Industrial and Applied Mathematics, 25(4):196–220, 2021. doi:10.12941/jksiam.2021.25.196.

  8. M. Nonino, F. Ballarin, and G. Rozza. A monolithic and a partitioned, reduced basis method for fluid-structure interaction problems. Fluids, 6(6):229, 2021. doi:10.3390/fluids6060229.

  9. M. Strazzullo. Model Order Reduction for Nonlinear and Time-Dependent Parametric Optimal Flow Control Problems. PhD thesis, Mathematical Analysis, Modelling, and Applications, SISSA, Italy, Sep. 2021. URL: http://hdl.handle.net/20.500.11767/124559.

  10. M. Strazzullo, F. Ballarin, and G. Rozza. A certified reduced basis method for linear parametrized parabolic optimal control problems in space-time formulation. Submitted, 2021. arXiv:2103.00460.

  11. M. Strazzullo, Z. Zainib, F. Ballarin, and G. Rozza. Reduced order methods for parametrized non-linear and time dependent optimal flow control problems, towards applications in biomedical and environmental sciences. In F. J. Vermolen and C. Vuik, editors, Numerical Mathematics and Advanced Applications ENUMATH 2019, 841–850. Springer International Publishing, 2021. doi:10.1007/978-3-030-55874-1_83.

  12. Z. Zainib, F. Ballarin, S. Fremes, P. Triverio, L. Jiménez-Juan, and G. Rozza. Reduced order methods for parametric optimal flow control in coronary bypass grafts, towards patient-specific data assimilation. International Journal for Numerical Methods in Biomedical Engineering, 37(12):e3367, 2021. doi:10.1002/cnm.3367.

  13. M. Zancanaro, F. Ballarin, S. Perotto, and G. Rozza. Hierarchical model reduction techniques for flow modeling in a parametrized setting. Multiscale Modeling & Simulation, 19(1):267–293, 2021. doi:10.1137/19M1285330.

Year 2020

  1. S. Ali, F. Ballarin, and G. Rozza. Stabilized reduced basis methods for parametrized steady stokes and navier-stokes equations. Computers & Mathematics with Applications, 80(11):2399–2416, 2020. doi:10.1016/j.camwa.2020.03.019.

  2. C. Bigoni. Numerical methods for structural anomaly detection using model order reduction and data-driven techniques. PhD thesis, Mathematics, EPFL, Lausanne, Switzerland, Sep. 2020. URL: https://infoscience.epfl.ch/record/279985.

  3. C. Bigoni and J. S. Hesthaven. Simulation-based anomaly detection and damage localization: an application to structural health monitoring. Computer Methods in Applied Mechanics and Engineering, 363:112896, 2020. doi:10.1016/j.cma.2020.112896.

  4. S. Hijazi, S. Ali, G. Stabile, F. Ballarin, and G. Rozza. The effort of increasing Reynolds number in projection-based reduced order methods: from laminar to turbulent flows. In H. van Brummelen, A. Corsini, S. Perotto, and G. Rozza, editors, Numerical Methods for Flows: FEF 2017 Selected Contributions, pages 245–264. Springer International Publishing, 2020. doi:10.1007/978-3-030-30705-9_22.

  5. E. N. Karatzas, F. Ballarin, and G. Rozza. Projection-based reduced order models for a cut finite element method in parametrized domains. Computers & Mathematics with Applications, 79(3):833–851, 2020. doi:10.1016/j.camwa.2019.08.003.

  6. D. V. P. Montag. Numerical approximation of inverse problems for pdes via neural network augmentation. Master's thesis, Mathematical Engineering, Politecnico di Milano, Italy, Oct. 2020. URL: https://www.politesi.polimi.it/bitstream/10589/166565/3/Montag_Thesis.pdf.

  7. M. Nonino. On the application of the Reduced Basis Method to Fluid-Structure Interaction problems. PhD thesis, Mathematical Analysis, Modelling, and Applications, SISSA, Italy, Sep. 2020. URL: http://hdl.handle.net/20.500.11767/114309.

  8. F. Pichi. Reduced order models for parametric bifurcation problems in nonlinear PDEs. PhD thesis, Mathematical Analysis, Modelling, and Applications, SISSA, Italy, Sep. 2020. URL: http://hdl.handle.net/20.500.11767/114329.

  9. F. Pichi, A. Quaini, and G. Rozza. A reduced order technique to study bifurcating phenomena: application to the Gross-Pitaevskii equation. SIAM Journal on Scientific Computing, 42(5):B1115–B1135, 2020. doi:10.1137/20M1313106.

  10. M. Strazzullo, F. Ballarin, and G. Rozza. POD–Galerkin model order reduction for parametrized time dependent linear quadratic optimal control problems in saddle point formulation. Journal of Scientific Computing, 83(3):55, 2020. doi:10.1007/s10915-020-01232-x.

Year 2019

  1. D. Baroli and A. Zilian. Model order reduction applied to ALE-fluid dynamics. PAMM, 19(1):e201900437, 2019. doi:10.1002/pamm.201900437.

  2. J. Genovese. Reduced order methods for uncertainty quantification in computational fluid dynamics. Master's thesis, Mathematical Engineering, Politecnico di Torino, Italy, Oct. 2019. URL: https://webthesis.biblio.polito.it/11989/1/tesi.pdf.

  3. M. Gunzburger, T. Iliescu, M. Mohebujjaman, and M. Schneier. An evolve-filter-relax stabilized reduced order stochastic collocation method for the time-dependent navier–stokes equations. SIAM/ASA Journal on Uncertainty Quantification, 7(4):1162–1184, 2019. doi:10.1137/18M1221618.

  4. M. Gunzburger, M. Schneier, C. Webster, and G. Zhang. An improved discrete least-squares/reduced-basis method for parameterized elliptic pdes. Journal of Scientific Computing, 81(1):76–91, 2019. doi:10.1007/s10915-018-0661-6.

  5. F. Pichi and G. Rozza. Reduced basis approaches for parametrized bifurcation problems held by non-linear von kármán equations. Journal of Scientific Computing, 81(1):112–135, 2019. doi:10.1007/s10915-019-01003-3.

  6. G. Stabile, F. Ballarin, G. Zuccarino, and G. Rozza. A reduced order variational multiscale approach for turbulent flows. Advances in Computational Mathematics, 45(5):2349–2368, 2019. doi:10.1007/s10444-019-09712-x.

  7. L. Venturi, F. Ballarin, and G. Rozza. A weighted POD method for elliptic PDEs with random inputs. Journal of Scientific Computing, 81(1):136–153, 2019. doi:10.1007/s10915-018-0830-7.

  8. L. Venturi, D. Torlo, F. Ballarin, and G. Rozza. Weighted reduced order methods for parametrized partial differential equations with random inputs. In F. Canavero, editor, Uncertainty Modeling for Engineering Applications, pages 27–40. Springer International Publishing, 2019. doi:10.1007/978-3-030-04870-9_2.

  9. Z. Zainib. Reduced order parametrized viscous optimal flow control problems and applications in coronary artery bypass grafts with patient-specific geometrical reconstruction and data assimilation. PhD thesis, Mathematical Analysis, Modelling, and Applications, SISSA, Italy, Sep. 2019. URL: http://hdl.handle.net/20.500.11767/103036.

Year 2018

  1. S. Ali. Stabilized reduced basis methods for the approximation of parametrized viscous flows. PhD thesis, Mathematical Analysis, Modelling, and Applications, SISSA, Italy, Sep. 2018. URL: http://hdl.handle.net/20.500.11767/82794.

  2. D. B. P. Huynh, F. Pichi, and G. Rozza. Reduced basis approximation and a posteriori error estimation: applications to elasticity problems in several parametric settings, pages 203–247. Volume 15. Springer International Publishing, 2018. doi:10.1007/978-3-319-94676-4_8.

  3. M. Strazzullo, F. Ballarin, R. Mosetti, and G. Rozza. Model reduction for parametrized optimal control problems in environmental marine sciences and engineering. SIAM Journal on Scientific Computing, 40(4):B1055–B1079, 2018. doi:10.1137/17M1150591.

  4. M. Tezzele, F. Ballarin, and G. Rozza. Combined parameter and model reduction of cardiovascular problems by means of active subspaces and POD-Galerkin methods. In D. Boffi, L. F. Pavarino, G. Rozza, S. Scacchi, and C. Vergara, editors, Mathematical and Numerical Modeling of the Cardiovascular System and Applications, pages 185–207. Springer International Publishing, Cham, 2018. doi:10.1007/978-3-319-96649-6_8.

  5. D. Torlo, F. Ballarin, and G. Rozza. Stabilized weighted reduced basis methods for parametrized advection dominated problems with random inputs. SIAM/ASA Journal on Uncertainty Quantification, 6(4):1475–1502, 2018. doi:10.1137/17M1163517.

Year 2017

  1. F. Ballarin, G. Rozza, and Y. Maday. Reduced-order semi-implicit schemes for fluid-structure interaction problems. In P. Benner, M. Ohlberger, A. Patera, G. Rozza, and K. Urban, editors, Model Reduction of Parametrized Systems, volume 17, pages 149–167. Springer International Publishing, 2017. doi:10.1007/978-3-319-58786-8_10.

  2. G. Meglioli. Comparison of model order reduction approaches in parametrized optimal control problems. Master's thesis, Mathematical Engineering, Politecnico di Milano, Italy, Dec. 2017. URL: http://hdl.handle.net/10589/137279.

  3. M. Strazzullo. Reduced order methods for parametric optimal flow control problems: applications in environmental and marine sciences and engineering. Master's thesis, Mathematics, University of Trieste, Italy, Mar. 2017. URL: http://people.sissa.it/~grozza/wp-content/uploads/2018/03/Strazzullo.pdf.

  4. M. Zancanaro. Hierarchical model reduction techniques for flows in a parametric setting. Master's thesis, Aeronautical Engineering, Politecnico di Milano, Italy, Apr. 2017. URL: https://www.politesi.polimi.it/handle/10589/134020.

Year 2016

  1. F. Salmoiraghi, F. Ballarin, G. Corsi, A. Mola, M. Tezzele, and G. Rozza. Advances in geometrical parametrization and reduced order models and methods for computational fluid dynamics problems in applied sciences and engineering: overview and perspectives. In M. Papadrakakis, V. Papadopoulos, G. Stefanou, and V. Plevris, editors, Proceedings of the ECCOMAS Congress 2016, VII European Conference on Computational Methods in Applied Sciences and Engineering. 2016. doi:10.7712/100016.1867.8680.

  2. A. Sartori, A. Cammi, L. Luzzi, and G. Rozza. A reduced basis approach for modeling the movement of nuclear reactor control rods. Journal of Nuclear Engineering and Radiation Science, 02 2016. doi:10.1115/1.4031945.

  3. D. Torlo. Stabilized reduced basis method for transport PDEs with random inputs. Master's thesis, Mathematics, University of Trieste, Italy, Jul. 2016. URL: http://people.sissa.it/~grozza/wp-content/uploads/2018/03/torlo.pdf.

  4. L. Venturi. Weighted reduced order methods for parametrized PDEs in uncertainty quantification problems. Master's thesis, Mathematics, University of Trieste, Italy, Jul. 2016. URL: http://people.sissa.it/~grozza/wp-content/uploads/2018/03/venturi.pdf.

Year 2015

  1. J. S. Hesthaven, G. Rozza, and B. Stamm. Certified Reduced Basis Methods for Parametrized Partial Differential Equations. SpringerBriefs in Mathematics. Springer International Publishing, 2015. ISBN 978-3-319-22469-5.