Accuracy analysis of explicit Runge-Kutta methods applied to the incompressible Navier-Stokes equations. - Benjamin Sanderse; Barry Koren (J. Comput. Phys., 2012)

Energy-conserving Runge-Kutta methods for the incompressible Navier-Stokes equations. - Benjamin Sanderse (J. Comput. Phys., 2013)

Boundary treatment for fourth-order staggered mesh discretizations of the incompressible Navier-Stokes equations. - Benjamin Sanderse; R. W. C. P. Verstappen; Barry Koren (J. Comput. Phys., 2014)

Digital Twins - Introduction to the Special Theme. - Edgar R. Weippl; Benjamin Sanderse (ERCIM News, 2018)

Fatigue design load calculations of the offshore NREL 5MW benchmark turbine using quadrature rule techniques. - Laurent M. M. van den Bos; Wim Bierbooms; Armando Alexandre; Benjamin Sanderse; Gerard J. W. van Bussel (CoRR, 2019)

PDE/PDF-informed adaptive sampling for efficient non-intrusive surrogate modelling. - Yous van Halder; Benjamin Sanderse; Barry Koren (CoRR, 2019)

Constraint-consistent Runge-Kutta methods for one-dimensional incompressible multiphase flow. - Benjamin Sanderse; Arthur E. P. Veldman (J. Comput. Phys., 2019)

An Adaptive Minimum Spanning Tree Multielement Method for Uncertainty Quantification of Smooth and Discontinuous Responses. - Yous van Halder; Benjamin Sanderse; Barry Koren (SIAM J. Sci. Comput., 2019)

Multi-level neural networks for PDEs with uncertain parameters. - Yous van Halder; Benjamin Sanderse; Barry Koren (CoRR, 2020)

Faster Flow Predictions with Intrusive Neural Networks. - Yous van Halder; Benjamin Sanderse (ERCIM News, 2020)

Adaptive sampling-based quadrature rules for efficient Bayesian prediction. - Laurent M. M. van den Bos; Benjamin Sanderse; W. A. A. M. Bierbooms (J. Comput. Phys., 2020)

Non-linearly stable reduced-order models for incompressible flow with energy-conserving finite volume methods. - Benjamin Sanderse (J. Comput. Phys., 2020)

Generating Nested Quadrature Rules with Positive Weights based on Arbitrary Sample Sets. - Laurent M. M. van den Bos; Benjamin Sanderse; Wim Bierbooms; Gerard J. W. van Bussel (SIAM/ASA J. Uncertain. Quantification, 2020)

Energy-conserving formulation of the two-fluid model for incompressible two-phase flow in channels and pipes. - J. F. H. Buist; Benjamin Sanderse; S. Dubinkina; R. A. W. M. Henkes; Cornelis W. Oosterlee (CoRR, 2021)

A geometrical interpretation of the addition of nodes to an interpolatory quadrature rule while preserving positive weights. - Laurent M. M. van den Bos; Benjamin Sanderse (J. Comput. Appl. Math., 2021)

A novel pressure-free two-fluid model for one-dimensional incompressible multiphase flow. - Benjamin Sanderse; J. F. H. Buist; R. A. W. M. Henkes (J. Comput. Phys., 2021)

Uncertainty quantification and sensitivity analysis of COVID-19 exit strategies in an individual-based transmission model. - Federica Gugole; Luc E. Coffeng; Wouter Edeling; Benjamin Sanderse; Sake J. De Vlas; Daan Crommelin (PLoS Comput. Biol., 2021)

Momentum-conserving ROMs for the incompressible Navier-Stokes equations. - Henrik K. E. Rosenberger; Benjamin Sanderse (CoRR, 2022)

Learning filtered discretization operators: non-intrusive versus intrusive approaches. - Syver Døving Agdestein; Benjamin Sanderse (CoRR, 2022)

Adjoint Optimisation for Wind Farm Flow Control with a Free-Vortex Wake Model. - Maarten J. van den Broek; Delphine De Tavernier; Benjamin Sanderse; Jan-Willem van Wingerden (CoRR, 2022)

Optimal Control for Wind Turbine Wake Mixing on Floating Platforms. - Maarten J. van den Broek; Daniel van den Berg; Benjamin Sanderse; Jan-Willem van Wingerden (CoRR, 2022)

Comparison of neural closure models for discretised PDEs. - Hugo Melchers; Daan Crommelin; Barry Koren; Vlado Menkovski; Benjamin Sanderse (Comput. Math. Appl., 2023)

Energy-consistent discretization of viscous dissipation with application to natural convection flow. - Benjamin Sanderse; Francesc Xavier Trias (CoRR, 2023)

A pressure-free long-time stable reduced-order model for two-dimensional Rayleigh-Bénard convection. - Krishan Chand; Henrik K. E. Rosenberger; Benjamin Sanderse (CoRR, 2023)

Energy-stable discretization of the one-dimensional two-fluid model. - J. F. H. Buist; Benjamin Sanderse; S. Dubinkina; Cornelis W. Oosterlee; R. A. W. M. Henkes (CoRR, 2023)

Markov chain generative adversarial neural networks for solving Bayesian inverse problems in physics applications. - Nikolaj Takata Mücke; Benjamin Sanderse; Sander M. Bohté; Cornelis W. Oosterlee (Comput. Math. Appl., 2023)

No pressure? Energy-consistent ROMs for the incompressible Navier-Stokes equations with time-dependent boundary conditions. - Henrik K. E. Rosenberger; Benjamin Sanderse (J. Comput. Phys., 2023)

Scientific machine learning for closure models in multiscale problems: a review. - Benjamin Sanderse; Panos Stinis; Romit Maulik; Shady E. Ahmed (CoRR, 2024)

Entropy-Stable Model Reduction of One-Dimensional Hyperbolic Systems using Rational Quadratic Manifolds. - Robin Klein; Benjamin Sanderse; Pedro Costa 006; Rene Pecnik; Ruud Henkes (CoRR, 2024)

A new temperature evolution equation that enforces thermodynamic vapour-liquid equilibrium in multiphase flows - application to CO2 modeling. - Pardeep Kumar; Benjamin Sanderse; Patricio I. Rosen Esquivel; R. A. W. M. Henkes (CoRR, 2024)

Modeling Advection-Dominated Flows with Space-Local Reduced-Order Models. - Toby van Gastelen; Wouter Edeling; Benjamin Sanderse (CoRR, 2024)

Energy-conserving neural network for turbulence closure modeling. - Toby van Gastelen; Wouter Edeling; Benjamin Sanderse (J. Comput. Phys., 2024)

Energy-conserving hyper-reduction and temporal localization for reduced order models of the incompressible Navier-Stokes equations. - R. B. Klein; Benjamin Sanderse (J. Comput. Phys., 2024)

Discretize first, filter next: Learning divergence-consistent closure models for large-eddy simulation. - Syver Døving Agdestein; Benjamin Sanderse (J. Comput. Phys., 2025)

Entropy-stable model reduction of one-dimensional hyperbolic systems using rational quadratic manifolds. - R. B. Klein; Benjamin Sanderse; Pedro Costa 006; Rene Pecnik; R. A. W. M. Henkes (J. Comput. Phys., 2025)

Physics-aware generative models for turbulent fluid flows through energy-consistent stochastic interpolants. - Nikolaj Takata Mücke; Benjamin Sanderse (CoRR, 2025)

Energy-Conserving Neural Network Closure Model for Long-Time Accurate and Stable LES. - Toby van Gastelen; Wouter Edeling; Benjamin Sanderse (CoRR, 2025)

Harnessing Equivariance: Modeling Turbulence with Graph Neural Networks. - Marius Kurz; Andrea Beck; Benjamin Sanderse (CoRR, 2025)

Exact operator inference with minimal data. - Henrik K. E. Rosenberger; Benjamin Sanderse; Giovanni Stabile (CoRR, 2025)

Exact closure for discrete large-eddy simulation. - Syver Døving Agdestein; Roel W. C. P. Verstappen; Benjamin Sanderse (CoRR, 2025)

A new data-driven energy-stable Evolve-Filter-Relax model for turbulent flow simulation. - Anna Ivagnes; Toby van Gastelen; Syver Døving Agdestein; Benjamin Sanderse; Giovanni Stabile; Gianluigi Rozza (CoRR, 2025)