33 citations · 72 across the 7 of their papers we have counts for
7 papers
Comparison of neural closure models for discretised PDEs
Hugo Melchers, Daan Crommelin, Barry Koren +2
Neural closure models have recently been proposed as a method for efficiently approximating small scales in multiscale systems with neural networks. The choice of loss function and…
Intrusive deconvolutional neural networks for enhancing PIC/FLIP solutions
Y. van Halder, B. Sanderse, B. Koren
Traditional fluid flow predictions require large computational resources. Despite recent progress in parallel and GPU computing, the ability to run fluid flow predictions in real-t…
Multi-level neural networks for PDEs with uncertain parameters
Yous van Halder, Benjamin Sanderse, Barry Koren
A novel multi-level method for partial differential equations with uncertain parameters is proposed. The principle behind the method is that the error between grid levels in multi-…
Non-linearly stable reduced-order models for incompressible flow with energy-conserving finite volume methods
B. Sanderse
A novel reduced-order model (ROM) formulation for incompressible flows is presented with the key property that it exhibits non-linearly stability, independent of the mesh (of the f…
PDE/PDF-informed adaptive sampling for efficient non-intrusive surrogate modelling
Yous van Halder, Benjamin Sanderse, Barry Koren
A novel refinement measure for non-intrusive surrogate modelling of partial differential equations (PDEs) with uncertain parameters is proposed. Our approach uses an empirical inte…
Constraint-consistent Runge-Kutta methods for one-dimensional incompressible multiphase flow
Benjamin Sanderse, Arthur E. P. Veldman
New time integration methods are proposed for simulating incompressible multiphase flow in pipelines described by the one-dimensional two-fluid model. The methodology is based on '…