Balaje Kalyanaraman Postdoc at Umeå University, Sweden

20 August 2021

# Wrapping up GSoC

This is the final post in the Google Summer of Code 2021 series, and here I wish to summarize the project. This post is my official submission of my work for the final evaluation. During the summer of code, I wrote five blog posts related to the project which can be found on the my GSoC webpage. Along with the posts, I have also posted the progress report which can be found at the end of the page. I will briefly go through the new features and list all the Pull Requests submitted to the organization’s main repository that contains my contribution. For demonstrating the new features added, I use the following script:

Sample code snippets in my GSoC repo

The script is also available as a GitHub gist. Clone and run the script:

shell> git clone https://gist.github.com/Balaje/fa9769bff8fc29945e1220aa29e93afa blog-script
shell> cd blog-script/

julia> include("blog-script.jl")


## New features and merged PRs

• Evaluate FEFunction at arbitrary points (since Gridap release v.0.16.0):
• Fix evaluation for RT Elements (since Gridap release v.0.16.4):
• Interpolation between FESpace (since Gridap release v.0.16.4):

## Evaluating CellField on arbitrary point (Complete)

This is the first step in implementing the interpolation algorithm. After running the script, we type the following in the Julia REPL:

fhs = get_data(fₕ)
evaluate(fhs, Point(rand(2)))


In the first line, we extract the data using get_data from the FEFunction cell-wise, resulting in a LazyArray of LinearCombinationField. We can evaluate a LinearCombinationField at any point in Gridap. Now since finite element functions are defined locally, we need to search for the corresponding cell where the arbitrary point lies. Evaluating the “local” FEFunction at the cell gives the value of the FEFunction. The searching is done by building a KDTree from the set of points on the mesh and then finding the nearest neighbour. This searching mechanism has an $$O(\log(N))$$ complexity where $$N$$ being the number of points on the mesh. This is discussed in detail in my blog post:

Google Summer of Code 2021 - Community Bonding

Once we have the required finite element function fₕ defined on an FESpace,

domain = (0,1,0,1)
partition = (5,5)
model = CartesianDiscreteModel(domain, partition)
f₁(x) = x + x
reffe₁ = ReferenceFE(lagrangian, Float64, 1) # Lagrangian ReferenceFE
V₁ = FESpace(model, reffe₁) # Build the FESpace
fₕ = interpolate_everywhere(f₁,V₁)


evaluating fₕ on an arbitrary point is as simple as

fₕ(Point(rand(2))


The snippets for Lagrange FESpaces can be found here and the same can be found for RT elements here.

## Interpolation (Complete)

Now interpolation can be done using the new structure Interpolable available in Gridap. This takes advantage of the earlier mechanism of evaluating functions on arbitrary points. Once we define the Interpolable object corresponding to an FEFunction, we can dispatch the Interpolable object to the existing interpolate_everywhere method and interpolate the function onto the new space W₁ from V₁.

domain = (0,1,0,1)
partition = (20,20)
model = CartesianDiscreteModel(domain,partition)
W₁ = FESpace(model, reffe₁)

ifₕ = Interpolable(fₕ)
gₕ = interpolate_everywhere(ifₕ,W₁) # Interpolate fₕ ∈ V₁ on to W₁


We had a lot of back and forth on the interface and some of the possible implementations are available on the blog posts:

But we finally settled on the current interface using Interpolable after a long discussion in PR#632. Future work from here include optimizing the interface and reducing memory allocations even further. Sample snippets from blog-script.jl for demonstrating interpolation can be found for Lagrange elements here and Raviart Thomas Element here.

## Interpolation Matrix (Work In Progress)

Code to generate interpolation matrix. Sketched a way to do it for LagrangianDofBasis and a preliminary version is available on my GSoC Repo.

## Miscellaneous Examples

Example for testing the interpolation algorithm for n-Dimensional problems are available on my GSoC repo. I test the interface for a sinusoidal/random perturbation of the original mesh (code). The repository also contains examples of older implementations discussed in the earlier blog posts.

## Acknowledgements

I feel great to have worked on this project with Gridap this year! Many thanks to NumFOCUS, my mentors Eric, Santi, Oriol and the Gridap team. Looking forward to keep contributing to open-source and Gridap in the future.

tags: gridap - gsoc © 2019- Balaje Kalyanaraman. Hosted on Github pages. Based on the Minimal Theme by orderedlist