New preprints on efficient solution of multiple-network porous media models

Our new preprints on Parameter robust preconditioning for multi-compartmental Darcy equations and Parameter robust preconditioning by congruence for multiple-network poroelasticity are out on arXiv!

In these studies, we consider systems of equations describing flow in multiple, interacting fluid networks with different permeabilities. The resulting discrete systems of equations can easily become very large, and efficient solution techniques are key. In cases where the inter-network interactions dominate the intra-network permeabilities, we observed that the systems of equations became increasingly hard to solve. In particular, the number of solver iterations would increase with increasing discrete resolution and interaction parameters. However, by creating an algorithm for defining tailored changes of variables, we were able to define robust preconditioners with uniformly bounded iteration counts.

Personal favorite: The lovely trick for how to define parameter-tailored changes of variables via classical matrix analysis including simultaneous diagonalization by congruence.

Intracranial pressure elevation alters CSF clearance pathways

Our preprint on Intracranial pressure elevation alters CSF clearance pathways is now out on bioRxiv! 

Infusion testing is a common procedure to determine whether shunting will be beneficial in patients with normal pressure hydrocephalus. The method has a well-developed theoretical foundation and corresponding mathematical models that describe the CSF circulation from the choroid plexus to the arachnoid granulations. Here, we investigate to what extent the proposed glymphatic or paravascular pathway (or similar pathways) modifies the results of the traditional mathematical models.

We used a two-compartment model consisting of the subarachnoid space and the paravascular spaces. For the arachnoid granulations, the cribriform plate, capillaries and paravascular spaces, resistances were calculated and used to estimate flow before and during an infusion test. Next, pressure in the subarachnoid space and paravascular spaces were computed. Finally, different variations to the model were tested to evaluate the sensitivity of selected parameters.

At baseline, we found a very small paravascular flow directed into the subarachnoid space, while 60% of the fluid left through the arachnoid granulations and 40% left through the cribriform plate. However, during the infusion, paravascular flow reversed and 25% of the fluid left through these spaces, while 60% went through the arachnoid granulations and only 15% through the cribriform plate.

The relative distribution of CSF flow to different clearance pathways depends on intracranial pressure (ICP), with the arachnoid granulations as the main contributor to outflow. As such, ICP increase is an important factor that should be addressed when determining the pathways of injected substances in the subarachnoid space.

FEniCS install via Docker

I have finally updated my laptop from the arcane Ubuntu 16.04 to 18.04. First thing to configure: fluxbox :heart: of course. Second, xterm and emacs font, size and color. And third, custom FEniCS installation! Very easy this time around, but recording it here for future reference:

# Install curl 
sudo apt install curl

# Download FEniCS project script
curl -s | bash

# fenicsproject script is installed as /foo/.local/bin/fenicsproject
# Add to e.g. .bashrc:
# export PATH=/foo/.local/bin/fenicsproject:$PATH

# Check that you are using the expected version of fenicsproject, by
# examining output of
which fenicsproject

# Ready to go!
fenicsproject run

New pre-print on automated adjoints of coupled ODE-PDE systems

Our preprint on Automated adjoints of coupled ODE-PDE systems is now available on arXiv! Mathematical models that couple partial differential equations (PDEs) and spatially distributed ordinary differential equations (ODEs) arise in biology, medicine, chemistry and many other fields. In this paper we present extension to the FEniCS and dolfin-adjoint softwares for expressing and efficiently solving such coupled systems. Given an ODE described using an augmentation of the Unified Form Language (UFL) and a discretisation described by an arbitrary Butcher tableau, efficient code is automatically generated for the parallel solution of the ODE. The high-level description of the solution algorithm also facilitates the automatic derivation of the adjoint and tangent linearization of coupled PDE-ODE solvers. The supplementary code for the paper is also openly available.


Welcome Travis!

travisI am happy to announce that Dr. Travis Thompson joined the Waterscape project as a postdoctoral fellow at Simula Research Laboratory on June 8 2017.  Travis received a PhD in Mathematics from Texas A&M university in 2013; his research areas are numerical analysis, and scientific computing.  Recently published work is focused on the construction of a-priori error estimates, and solver development, for finite element methods applied to problems in computational fluid dynamics, and biomechanics. Within Waterscape, Travis will be studying a-priori and a-posteriori error analysis of mixed finite element methods for generalized poroelasticity aiming at accurate, robust and property-preserving methods.

Waterscales awarded 2016 ERC Starting Grant

I am truly grateful and honoured that the European Research Council has awarded me with a 5-year Starting Grant within Mathematics (PE1) to fund the Waterscales project, a project dedicated to the mathematical and computational foundations for modeling cerebral fluid flow.

The Waterscales vision

Over the next decades, mathematics and numerics could play a crucial role in gaining new insight into the mechanisms driving water transport through the brain. Indeed, medical doctors express an urgent need for multiscale modeling and simulation – to overcome fundamental limitations in traditional techniques. Surprisingly little attention has been paid to the numerics of the brain’s waterscape however, in stark contrast to the role of simulation in other fields of neuroscience, and key mathematical models and methods are missing. To address this important challenge, the overall ambition of the Waterscales project is to establish the mathematical, numerical and computational foundations for predictively modeling fluid flow and solute transport through the brain across spatiotemporal scales – from the cellular to the organ level.