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ORCIDBenjamin Sanderse
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- affiliation: Centrum Wiskunde & Informatica, Amsterdam, The Netherlands
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2020 – today
- 2025
[j18]Syver Døving Agdestein
, Benjamin Sanderse:
Discretize first, filter next: Learning divergence-consistent closure models for large-eddy simulation. J. Comput. Phys. 522: 113577 (2025)- [j17]R. B. Klein, Benjamin Sanderse, Pedro Costa, Rene Pecnik, R. A. W. M. Henkes:
Entropy-stable model reduction of one-dimensional hyperbolic systems using rational quadratic manifolds. J. Comput. Phys. 528: 113817 (2025) - [i31]Nikolaj Takata Mücke, Benjamin Sanderse:
Physics-aware generative models for turbulent fluid flows through energy-consistent stochastic interpolants. CoRR abs/2504.05852 (2025) - [i30]Toby van Gastelen, Wouter Edeling, Benjamin Sanderse:
Energy-Conserving Neural Network Closure Model for Long-Time Accurate and Stable LES. CoRR abs/2504.05868 (2025) - [i29]Marius Kurz, Andrea Beck, Benjamin Sanderse:
Harnessing Equivariance: Modeling Turbulence with Graph Neural Networks. CoRR abs/2504.07741 (2025) - [i28]Henrik K. E. Rosenberger, Benjamin Sanderse, Giovanni Stabile:
Exact operator inference with minimal data. CoRR abs/2506.01244 (2025) - [i27]Syver Døving Agdestein, Roel W. C. P. Verstappen, Benjamin Sanderse:
Exact closure for discrete large-eddy simulation. CoRR abs/2507.17051 (2025) - [i26]Anna Ivagnes, Toby van Gastelen, Syver Døving Agdestein, Benjamin Sanderse, Giovanni Stabile, Gianluigi Rozza:
A new data-driven energy-stable Evolve-Filter-Relax model for turbulent flow simulation. CoRR abs/2507.17423 (2025) - 2024
- [j16]R. B. Klein, Benjamin Sanderse:
Energy-conserving hyper-reduction and temporal localization for reduced order models of the incompressible Navier-Stokes equations. J. Comput. Phys. 499: 112697 (2024) - [j15]Toby van Gastelen, Wouter Edeling, Benjamin Sanderse:
Energy-conserving neural network for turbulence closure modeling. J. Comput. Phys. 508: 113003 (2024) - [i25]Benjamin Sanderse, Panos Stinis, Romit Maulik, Shady E. Ahmed:
Scientific machine learning for closure models in multiscale problems: a review. CoRR abs/2403.02913 (2024) - [i24]Syver Døving Agdestein, Benjamin Sanderse:
Discretize first, filter next: learning divergence-consistent closure models for large-eddy simulation. CoRR abs/2403.18088 (2024) - [i23]Robin Klein, Benjamin Sanderse, Pedro Costa, Rene Pecnik, Ruud Henkes:
Entropy-Stable Model Reduction of One-Dimensional Hyperbolic Systems using Rational Quadratic Manifolds. CoRR abs/2407.12627 (2024) - [i22]Pardeep Kumar, Benjamin Sanderse, Patricio I. Rosen Esquivel, R. A. W. M. Henkes:
A new temperature evolution equation that enforces thermodynamic vapour-liquid equilibrium in multiphase flows - application to CO2 modeling. CoRR abs/2408.09164 (2024) - [i21]Toby van Gastelen, Wouter Edeling, Benjamin Sanderse:
Modeling Advection-Dominated Flows with Space-Local Reduced-Order Models. CoRR abs/2409.08793 (2024) - 2023
- [j14]Hugo Melchers, Daan Crommelin, Barry Koren, Vlado Menkovski, Benjamin Sanderse:
Comparison of neural closure models for discretised PDEs. Comput. Math. Appl. 143: 94-107 (2023) - [j13]Nikolaj Takata Mücke, Benjamin Sanderse, Sander M. Bohté, Cornelis W. Oosterlee:
Markov chain generative adversarial neural networks for solving Bayesian inverse problems in physics applications. Comput. Math. Appl. 147: 278-299 (2023) - [j12]Henrik K. E. Rosenberger, Benjamin Sanderse:
No pressure? Energy-consistent ROMs for the incompressible Navier-Stokes equations with time-dependent boundary conditions. J. Comput. Phys. 491: 112405 (2023) - [i20]Toby van Gastelen, Wouter Edeling, Benjamin Sanderse:
Energy-Conserving Neural Network for Turbulence Closure Modeling. CoRR abs/2301.13770 (2023) - [i19]Benjamin Sanderse, Francesc Xavier Trias:
Energy-consistent discretization of viscous dissipation with application to natural convection flow. CoRR abs/2307.10874 (2023) - [i18]Krishan Chand, Henrik K. E. Rosenberger, Benjamin Sanderse:
A pressure-free long-time stable reduced-order model for two-dimensional Rayleigh-Bénard convection. CoRR abs/2307.11422 (2023) - [i17]J. F. H. Buist, Benjamin Sanderse, S. Dubinkina, Cornelis W. Oosterlee, R. A. W. M. Henkes:
Energy-stable discretization of the one-dimensional two-fluid model. CoRR abs/2310.13978 (2023) - 2022
- [i16]Henrik K. E. Rosenberger, Benjamin Sanderse:
Momentum-conserving ROMs for the incompressible Navier-Stokes equations. CoRR abs/2208.09360 (2022) - [i15]Syver Døving Agdestein, Benjamin Sanderse:
Learning filtered discretization operators: non-intrusive versus intrusive approaches. CoRR abs/2208.09363 (2022) - [i14]Maarten J. van den Broek, Delphine De Tavernier, Benjamin Sanderse, Jan-Willem van Wingerden:
Adjoint Optimisation for Wind Farm Flow Control with a Free-Vortex Wake Model. CoRR abs/2208.11516 (2022) - [i13]Hugo Melchers, Daan Crommelin, Barry Koren, Vlado Menkovski, Benjamin Sanderse:
Comparison of neural closure models for discretised PDEs. CoRR abs/2210.14675 (2022) - [i12]Maarten J. van den Broek, Daniel van den Berg, Benjamin Sanderse, Jan-Willem van Wingerden:
Optimal Control for Wind Turbine Wake Mixing on Floating Platforms. CoRR abs/2210.17347 (2022) - [i11]Henrik K. E. Rosenberger, Benjamin Sanderse:
No pressure? Energy-consistent ROMs for the incompressible Navier-Stokes equations with time-dependent boundary conditions. CoRR abs/2212.12036 (2022) - 2021
- [j11]Laurent M. M. van den Bos, Benjamin Sanderse:
A geometrical interpretation of the addition of nodes to an interpolatory quadrature rule while preserving positive weights. J. Comput. Appl. Math. 391: 113430 (2021) - [j10]Benjamin Sanderse, J. F. H. Buist, R. A. W. M. Henkes:
A novel pressure-free two-fluid model for one-dimensional incompressible multiphase flow. J. Comput. Phys. 426: 109919 (2021) - [j9]Federica Gugole, Luc E. Coffeng, Wouter Edeling, Benjamin Sanderse, Sake J. De Vlas, Daan Crommelin:
Uncertainty quantification and sensitivity analysis of COVID-19 exit strategies in an individual-based transmission model. PLoS Comput. Biol. 17(9) (2021) - [i10]J. F. H. Buist, Benjamin Sanderse, S. Dubinkina, R. A. W. M. Henkes, Cornelis W. Oosterlee:
Energy-conserving formulation of the two-fluid model for incompressible two-phase flow in channels and pipes. CoRR abs/2104.07728 (2021) - [i9]Nikolaj Takata Mücke, Benjamin Sanderse, Sander M. Bohté, Cornelis W. Oosterlee:
Markov Chain Generative Adversarial Neural Networks for Solving Bayesian Inverse Problems in Physics Applications. CoRR abs/2111.12408 (2021) - 2020
- [j8]Laurent M. M. van den Bos, Benjamin Sanderse, W. A. A. M. Bierbooms:
Adaptive sampling-based quadrature rules for efficient Bayesian prediction. J. Comput. Phys. 417: 109537 (2020) - [j7]Benjamin Sanderse:
Non-linearly stable reduced-order models for incompressible flow with energy-conserving finite volume methods. J. Comput. Phys. 421: 109736 (2020) - [j6]Laurent M. M. van den Bos, Benjamin Sanderse, Wim Bierbooms, Gerard J. W. van Bussel:
Generating Nested Quadrature Rules with Positive Weights based on Arbitrary Sample Sets. SIAM/ASA J. Uncertain. Quantification 8(1): 139-169 (2020) - [i8]Benjamin Sanderse, J. F. H. Buist, R. A. W. M. Henkes:
A novel pressure-free two-fluid model for one-dimensional incompressible multiphase flow. CoRR abs/2004.06987 (2020) - [i7]Yous van Halder, Benjamin Sanderse, Barry Koren:
Multi-level neural networks for PDEs with uncertain parameters. CoRR abs/2004.13128 (2020) - [i6]Yous van Halder, Benjamin Sanderse:
Faster Flow Predictions with Intrusive Neural Networks. ERCIM News 2020(122) (2020)
2010 – 2019
- 2019
- [j5]Benjamin Sanderse, Arthur E. P. Veldman:
Constraint-consistent Runge-Kutta methods for one-dimensional incompressible multiphase flow. J. Comput. Phys. 384: 170-199 (2019) - [j4]Yous van Halder, Benjamin Sanderse, Barry Koren:
An Adaptive Minimum Spanning Tree Multielement Method for Uncertainty Quantification of Smooth and Discontinuous Responses. SIAM J. Sci. Comput. 41(6): A3624-A3648 (2019) - [i5]Laurent M. M. van den Bos, Wim Bierbooms, Armando Alexandre, Benjamin Sanderse, Gerard J. W. van Bussel:
Fatigue design load calculations of the offshore NREL 5MW benchmark turbine using quadrature rule techniques. CoRR abs/1904.07021 (2019) - [i4]Yous van Halder, Benjamin Sanderse, Barry Koren:
PDE/PDF-informed adaptive sampling for efficient non-intrusive surrogate modelling. CoRR abs/1907.04022 (2019) - [i3]Laurent M. M. van den Bos, Benjamin Sanderse, W. A. A. M. Bierbooms:
Adaptive sampling-based quadrature rules for efficient Bayesian prediction. CoRR abs/1907.08418 (2019) - [i2]Benjamin Sanderse:
Non-linearly stable reduced-order models for incompressible flow with energy-conserving finite volume methods. CoRR abs/1909.11462 (2019) - 2018
- [i1]Edgar R. Weippl, Benjamin Sanderse:
Digital Twins - Introduction to the Special Theme. ERCIM News 2018(115) (2018) - 2014
- [j3]Benjamin Sanderse, R. W. C. P. Verstappen, Barry Koren:
Boundary treatment for fourth-order staggered mesh discretizations of the incompressible Navier-Stokes equations. J. Comput. Phys. 257: 1472-1505 (2014) - 2013
- [j2]Benjamin Sanderse:
Energy-conserving Runge-Kutta methods for the incompressible Navier-Stokes equations. J. Comput. Phys. 233: 100-131 (2013) - 2012
- [j1]Benjamin Sanderse, Barry Koren:
Accuracy analysis of explicit Runge-Kutta methods applied to the incompressible Navier-Stokes equations. J. Comput. Phys. 231(8): 3041-3063 (2012)
Coauthor Index
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