dolfinx VS pykokkos

One thing that is a pain when learning this stuff is that actually performing the optimization requires a good understanding of the numerical discretization of PDEs. Finite elements are a natural choice because it is very easy to characterize the adjoint with this formulation. There are some good free tools that you can use to actually learn and do some computations yourself. The first is hIPPYlib (paper, code), which is built on top of FEniCS (link), for which there are many good tutorials. Beware trying to install this on Windows though. You will need to work in Docker or in Ubuntu via Windows Linux Subsystem.

If you're ok with coding, fenics is a solid place to start. Also if you're comfortable with coding, openfoam is FVM, rather than FEM, but it can handle solidmechanics.

> FEniCs made FEM so easy

https://fenicsproject.org/

Indeed, was blown away when I saw it for the first time over a decade ago, compared to the convoluted C++ FEM libraries I had seen before that.

Have you looked at FEniCS? Pretty much everything else I'm aware of is probably overkill (e.g., MOOSE in C++, HYPRE's Python bindings, etc.)

FEniCSx is quite good.

You mean Python? For many research tasks it's fine. High level libraries let you define your computation in a minimal amount of code. FEniCS is a great example of this - underneath it compiles the abstracted high level stuff to calls to low-level libraries that do the heavy lifting. For many applications you can just write vectorized code with Numpy that performs well, or use Numba to JIT what you can't vectorize. For some tasks, however, you need interfaces that don't exist in the high level libraries, and that was the case for me.

I just came across FEniCSX. I’m not sure if it’s what you want but here’s the description:

Fenics: Very popular finite element framework “UseTab: Never” https://github.com/FEniCS/dolfinx/blob/main/.clang-format

I see more and more Pythonic solvers due to JIT and similar techniques (Disclaimer: I don't use them to make solvers). This is an old diagram what you can use in your implementation. As I use Kokkos for two of my projects, I find attractive this new pykokkos thing. But there are specific libraries (dialects?) for numerics: like Taichi, Fenics and DOLFINx, etc.

Making these bindings available was a critical step in the progress toward writing Kokkos in native Python via pykokkos, which is currently available for Linux + Python 3.8 + GCC 7.5 + CUDA 10.2 toolchains.