B. Sanderse

GertJan van Heijst, Yous van Halder, Benjamin Sanderse, 2019, Proceedings of the 3rd International Conference on Uncertainty Quantification in Computational Sciences and Engineering (UNCECOMP 2019).

Energy-conserving discretization methods for the incompressible Navier-Stokes equations : application to the simulation of wind-turbine wakes

B. Sanderse, B. Sanderse, 2013 .

Energy-conserving Runge-Kutta methods for the incompressible Navier-Stokes equations

Benjamin Sanderse, B. Sanderse, 2013, J. Comput. Phys..

Accuracy analysis of explicit Runge-Kutta methods applied to the incompressible Navier-Stokes equations

Barry Koren, Benjamin Sanderse, B. Koren, 2012, J. Comput. Phys..

New explicit Runge-Kutta methods for the incompressible Navier-Stokes equations

B. Koren, B. Sanderse, 2012 .

Uncertainty quantification and sensitivity analysis of COVID-19 exit strategies in an individual-based transmission model

L. Coffeng, D. Crommelin, W. Edeling, 2021, medRxiv.

Bayesian Model Calibration with Interpolating Polynomials based on Adaptively Weighted Leja Nodes

B. Sanderse, W.A.A.M. Bierbooms, L.M.M. van den Bos, 2018, Communications in Computational Physics.

Energy-Conserving Navier-Stokes Solver. Verification of steady laminar flows

B. Sanderse, B. Sanderse, 2011 .

Energy-Conserving Neural Network for Turbulence Closure Modeling

W. Edeling, B. Sanderse, Toby van Gastelen, 2023, Journal of Computational Physics.

Structure-Preserving Hyper-Reduction and Temporal Localization for Reduced Order Models of Incompressible Flows

B. Sanderse, Robin Ben Klein, R. B. Klein, 2023, SSRN Electronic Journal.

Non-linearly stable reduced-order models for incompressible flow with energy-conserving finite volume methods

B. Sanderse, 2019, J. Comput. Phys..

Uncertainty quantification for wind energy applications

B. Sanderse, L.M.M. van deBos, B. Sanderse, 2017 .

Review of CFD for wind-turbine wake aerodynamics

B. Sanderse, B. Koren, B. Sanderse, 2010 .

Boundary treatment for fourth-order staggered mesh discretizations of the incompressible Navier-Stokes equations

Barry Koren, Benjamin Sanderse, R. W. C. P. Verstappen, 2014, J. Comput. Phys..

Generating Nested Quadrature Rules with Positive Weights based on Arbitrary Sample Sets

B. Sanderse, W.A.A.M. Bierbooms, L.M.M. van den Bos, 2018, SIAM/ASA J. Uncertain. Quantification.

Constraint-consistent Runge-Kutta methods for one-dimensional incompressible multiphase flow

Benjamin Sanderse, Arthur E. P. Veldman, A. Veldman, 2018, J. Comput. Phys..

Efficient simulation of one-dimensional two-phase flow with a high-order h -adaptive space-time Discontinuous Galerkin method

Cornelis Vuik, B. Sanderse, J.S.B. Van Zwieten, 2017 .

Comparison of neural closure models for discretised PDEs

D. Crommelin, B. Koren, Vlado Menkovski, 2022, Comput. Math. Appl..

Energy-consistent discretization of viscous dissipation with application to natural convection flow

F. Trias, B. Sanderse, 2023, ArXiv.

Efficient ultimate load estimation for offshore wind turbines using interpolating surrogate models

L. M. M. van den Bos, G.J.W. Van Bussel, B. Sanderse, 2018, Journal of Physics: Conference Series.

Delft University of Technology Efficient Bayesian calibration of aerodynamic wind turbine models using surrogate modeling

Benjamin Sanderse, Vinit V. Dighe, Koen Boorsma, 2022 .

Intrusive deconvolutional neural networks for enhancing PIC/FLIP solutions

B. Koren, B. Sanderse, Y. V. Halder, 2021, 2106.03491.

Adaptive sampling-based quadrature rules for efficient Bayesian prediction

L. M. M. van den Bos, B. Sanderse, W.A.A.M. Bierbooms, 2019, J. Comput. Phys..

Delft University of Technology Bayesian model calibration with interpolating polynomials based on adaptively weighted leja nodes

B. Sanderse, G. Bussel, W. Bierbooms, 2019 .

Fatigue design load calculations of the offshore NREL 5MW benchmark turbine using quadrature rule techniques pdf

L. M. M. van den Bos, Benjamin Sanderse, W. A. A. M. Bierbooms, 2019, ArXiv.

Learning filtered discretization operators: non-intrusive versus intrusive approaches

B. Sanderse, S. Agdestein, 2022, ArXiv.

Delft University of Technology Efficient Bayesian calibration of aerodynamic wind turbine models using surrogate modeling

G. Schepers, K. Boorsma, B. Sanderse, 2022 .

Sensitivity analysis and Bayesian calibration of a dynamic wind farm control model: FLORIDyn

B. Sanderse, T. Göçmen, J. Wingerden, 2022, Journal of Physics: Conference Series.

Energy-conserving hyper-reduction and temporal localization for reduced order models of the incompressible Navier-Stokes equations

B. Sanderse, R. B. Klein, B. Sanderse, 2023, J. Comput. Phys..

No pressure? Energy-consistent ROMs for the incompressible Navier-Stokes equations with time-dependent boundary conditions

B. Sanderse, Henrik K. E. Rosenberger, 2022, J. Comput. Phys..

Energy-stable discretization of the one-dimensional two-fluid model

C. Oosterlee, B. Sanderse, J. Buist, 2023, arXiv.org.

Energy‐consistent formulation of the pressure‐free two‐fluid model

C. Oosterlee, R. Henkes, B. Sanderse, 2022, International Journal for Numerical Methods in Fluids.

Energy-conserving formulation of the two-fluid model for incompressible two-phase flow in channels and pipes

C. Oosterlee, R. Henkes, B. Sanderse, 2021, Computers & Fluids.

Analysis of time integration methods for the compressible two-fluid model for pipe flow simulations

Ivar Eskerud Smith, B. Sanderse, B. Sanderse, 2017 .

A minimum-dissipation time-integration strategy for large-eddy simulation of incompressible turbulent flows

B. Sanderse, G. Coppola, Francesco Capuano, 2017 .

Analysis of shock relations for steady potential flow models

Barry Koren, B. Sanderse, B. Koren, 2013 .

A pressure-free long-time stable reduced-order model for two-dimensional Rayleigh-Bénard convection.

B. Sanderse, Krishan Chand, Henrik K. E. Rosenberger, 2023, Chaos.