74 citations · 75 across the 9 of their papers we have counts for
10 papers
Bayesian inference in active microrheology with wall and particle-particle interactions
Parajal Rai, Michelle M. A. Spanjaards, Ye Wang +2
Active microrheology infers rheological properties from the motion of a force-driven probe. In small samples, however, the probe is often close to walls or other probes, and the re…
Intrusive versus non-intrusive reduced-order modeling of generalized Newtonian fluid flows
Parajal Rai, Michelle Spanjaards, Patrick Anderson +2
This study compares three reduced-order modeling (ROM) approaches for flow simulations of generalized Newtonian fluids described by the Carreau rheological model. All three methods…
A comparison of Markov Chain Monte Carlo algorithms for Bayesian inference of constitutive models
Aricia Rinkens, Rodrigo L. S. Silva, Erik Quaeghebeur +2
Employing Bayesian inference to calibrate constitutive model parameters has grown substantially in recent years. Among the available techniques, Markov Chain Monte Carlo (MCMC) sam…
Identification of Port-Hamiltonian Differential-Algebraic Equations from Input-Output Data
N. Hagelaars, G. J. E. van Otterdijk, S. Moradi +3
Many models of physical systems, such as mechanical and electrical networks, exhibit algebraic constraints that arise from subsystem interconnections and underlying physical laws.…
Bayesian Model Selection for Complex Flows of Yield Stress Fluids
Aricia Rinkens, Clemens V. Verhoosel, Alexandra Alicke +2
Modeling yield stress fluids in complex flow scenarios presents significant challenges, particularly because conventional rheological characterization methods often yield material…
Port-Hamiltonian Neural Networks with Output Error Noise Models
Sarvin Moradi, Gerben I. Beintema, Nick Jaensson +2
Hamiltonian neural networks (HNNs) represent a promising class of physics-informed deep learning methods that utilize Hamiltonian theory as foundational knowledge within neural net…
Learning Subsystem Dynamics in Nonlinear Systems via Port-Hamiltonian Neural Networks
G. J. E. van Otterdijk, S. Moradi, S. Weiland +3
Port-Hamiltonian neural networks (pHNNs) are emerging as a powerful modeling tool that integrates physical laws with deep learning techniques. While most research has focused on mo…
Uncertainty quantification for the squeeze flow of generalized Newtonian fluids
Aricia Rinkens, Clemens V. Verhoosel, Nick O. Jaensson
The calibration of rheological parameters in the modeling of complex flows of non-Newtonian fluids can be a daunting task. In this paper we demonstrate how the framework of Uncerta…
Physics-Informed Learning Using Hamiltonian Neural Networks with Output Error Noise Models
Sarvin Moradi, Nick Jaensson, Roland Tóth +1
In order to make data-driven models of physical systems interpretable and reliable, it is essential to include prior physical knowledge in the modeling framework. Hamiltonian Neura…
Microscale Marangoni Surfers
Kilian Dietrich, Nick Jaensson, Ivo Buttinoni +2
We apply laser light to induce the asymmetric heating of Janus colloids adsorbed at water-oil interfaces and realize active micrometric "Marangoni surfers". The coupling of tempera…