Overview of Hackathon
The 2021 PyLith hackathon will be held online over 7 days, June 710 (MonThu) and June 1416 (MonWed), 2021. We plan to have 34 projects with 34 people working on each project. In addition to the list of potential projects below, prospective participants may also propose projects by posting a 12 page description under this topic.
All projects involve implementing new features in the code along with appropriate tests, examples, and documentation.
See the 2021 PyLith Hackathon webpage for additional details, including the registration process.
Recommended prerequisites for participating in the hackathon:

Desire to learn modern software development techniques

Comfortable with using the command line (terminal) on Unix operating systems

Familiar with C, C++, and/or Python

Familiar with boundary value problems and partial differential equations

Ability to spend time preparing for the hackathon

At least some knowledge of objectoriented programming
Potential Projects
Adaptive mesh refinement
Add support for adaptive mesh refinement (AMR) in PyLith by leveraging the AMR capabilities in PETSc. The main tasks include:

Remove any remaining dependencies of the problem specification on the finiteelement mesh.

Add the ability to switch to a different (refined) mesh within a given problem/simulation.

Develop an appropriate error metric that can be used to refine the mesh.
Earthquake cycle
Couple quasistatic interseismic and dynamic coseismic simulations to resolve the full dynamics of the earthquake cycle, including rupture propagation and radiated seismic waves and viscoelastic relaxation and aseismic creep.
The main tasks include:

Transferring the solution and state information from one problem/mesh/solver to another.

Creating a new problem type for earthquake cycle simulations with appropriate highlevel parameters.

Implement the appropriate trigger for switching between the quasistatic and dynamic solvers leveraging previous studies.
Poroelasticity
Extend the current poroelasticity implementation.
The main tasks include:

Add examples and benchmarks.

Add poroelastic effects for faulting,
Timedependent Green’s functions
Implement timedependent Green’s functions using either the traditional forward simulation or reciprocity and strain Green’s tensors.
The main tasks include:

Updating the PyLith output implementation to accomodate 4D arrays (Green’s function id, time, point, components).

Implementing the impulses (forward simulation case) or point loads (reciprocity with strain Green’s tensor approach).

Updating the GreensFn problem implementation to include time dependence.
Additional recommended prerequisites:

Comfortable writing code in Python and C++

Understanding of Green’s functions
Point dislocation earthquake sources
Implement point dislocation earthquake sources. This provide a point source implementation to complement the finitesource implementation of cohesive cells. Point dislocations are useful for modeling events whose rupture dimensions are small compared to the discretization size.
The main tasks include:

Implement point dislocations via a moment tensor.

Comfortable writing code in Python and C++

Understanding of Green’s functions
Import meshes from Gmsh
Add support for reading Gmsh files. This would include becoming familiar with using Gmsh to generate finiteelement mesh and updating several examples with Gmsh Python scripts.
Viscoelastic bulk rheologies using strain rate
The current Maxwell and powerlaw viscoelastic rheologies are formulated with strain as the primary variable. The timedependent nature of the viscoelastic relaxation is approximated with a series expansion. A more natural formulation is to use strain rate rather than strain as the primary variable. This project would add new implementations of the Maxwell, generalized Maxwell, and powerlaw viscoelastic bulk rheologies that use strain rate rather than strain as the primary variable.
Before the hackathon we expect that the project participants would work with the PyLith developers to derive the new bulk constitutive equations. The hackathon would be used to implement the new bulk constitutive models, create appropriate tests, benchmarks, and examples, and update the documentation.