38 citations · 259 across the 24 of their papers we have counts for
26 papers
A Model Hierarchy for Predicting the Flow in Stirred Tanks with Physics-Informed Neural Networks
Veronika Trávníková, Daniel Wolff, Nico Dirkes +3
This paper explores the potential of Physics-Informed Neural Networks (PINNs) to serve as Reduced Order Models (ROMs) for simulating the flow field within stirred tank reactors (ST…
Exploring the Influence of Parametrized Pulsatility on Left Ventricular Washout under LVAD Support: A Computational Study Using Reduced-Order Models
Maximilian R. Schuster, Nico Dirkes, Fabian Key +2
Medical therapy for patients with severe heart disease often relies on left ventricular assist devices (LVADs). It is an ongoing topic of research how complications like stagnation…
Neural Networks vs. Splines: Advances in Numerical Extruder Design
Jaewook Lee, Sebastian Hube, Stefanie Elgeti
We present a novel application of neural networks to design improved mixing elements for single-screw extruders. Specifically, we propose to use neural networks in numerical shape…
Investigation of reinforcement learning for shape optimization of profile extrusion dies
Clemens Fricke, Daniel Wolff, Marco Kemmerling +1
Profile extrusion is a continuous production process for manufacturing plastic profiles from molten polymer. Especially interesting is the design of the die, through which the melt…
A scale-coupled numerical method for transient close-contact melting
Leonardo Boledi, Fabian Key, Benjamin Terschanski +2
We introduce a numerical workflow to model and simulate transient close-contact melting processes based on the space-time finite element method. That is, we aim at computing the ve…
Computing the jump-term in space-time FEM for arbitrary temporal interpolation
Eugen Salzmann, Florian Zwicke, Stefanie Elgeti
One approach with rising popularity in analyzing time-dependent problems in science and engineering is the so-called space-time finite-element method that utilized finiteelements i…