chore:

1. docs
2. UpwindTest

README.md

@@ -9,3 +9,25 @@
 integrates Trixi.jl with Compiler-Based (LLVM level) automatic differentiation via [Enzyme.jl](https://github.com/EnzymeAD/Enzyme.jl)
 for hyperbolic partial differential equations (PDEs).
 This package was initialized through the **Google Summer of Code** program in 2024 and is still under development.
+
+See the [documentation](https://junyixu.github.io/TrixiEnzyme.jl/dev/) for more.
+
+## Installation
+To install the package, run the following command in the Julia REPL:
+```julia
+]  # enter Pkg mode
+(@v1.10) pkg> add https://github.com/junyixu/TrixiEnzyme.jl.git
+```
+Then simply run the following command to use the package:
+```julia
+using TrixiEnzyme
+```
+
+## Current status and TODOs
+
+- [x] General interface development
+- [x] Documentation
+- [ ] Including more AD Examples/Tutorials
+- [ ] GPU compatibility
+- [ ] Benchmarking
+    - WIP: [ISSUE#3](https://github.com/junyixu/jacobian4DG/issues/3)

docs/src/GSoC.md

@@ -9,11 +9,6 @@
 ## Project Overview
 Trixi.jl is a numerical simulation framework for conservation laws written in Julia. The integration of Trixi.jl with Compiler-Based (LLVM level) automatic differentiation via Enzyme.jl offers the following benefits: facilitates rapid forward mode AD, enables reverse mode AD, supports cross-language AD, and critically, supports mutating operations and caching, on which Trixi.jl relies, to enhance the performance of both simulation runs and AD. The final deliverable will include as many of Trixi's advanced features as possible, such as adaptive mesh refinement, shock capturing, etc., showcasing the benefits of differentiable programming in Julia's ecosystem.
 
-## Pre-GSoC24
-- The GSoC proposal: Integrating the Modern CFD Package Trixi.jl with Compiler-Based Auto-Diff via Enzyme.jl (see PDF)
-- Implemented AD with pure Julia personally using guidance from a [blog](https://blog.rogerluo.dev/2018/10/23/write-an-ad-in-one-day/)
-
-## GSoC24
 - **Forward Mode Automatic Differentiation (AD) for Discontinuous Galerkin Collocation Spectral Element Method (DGSEM)**: Implement forward mode automatic differentiation to enhance the calculation of derivatives in DG methods, improving computational efficiency and accuracy for various applications.
 - **Reverse Mode Automatic Differentiation for DG**.
 - **Improve Performance**:

src/jacobian.jl

@@ -1,7 +1,7 @@
 Enzyme.API.runtimeActivity!(true)
 
 """
-    jacobian_enzyme_forward_closure(semi::SemidiscretizationHyperbolic)
+    jacobian_enzyme_forward_closure(semi::SemidiscretizationHyperbolic; N = pick_batchsize(semi))
 
 Same as `jacobian_enzyme_forward` but with closure
 
@@ -207,7 +207,7 @@ function batch_mode_jacobian_enzyme_forward(f!::F, x::AbstractVector, N) where F
 end
 
 # %%
-function jacobian_enzyme_forward(semi::SemidiscretizationHyperbolic; N = pick_batchsize(x))
+function jacobian_enzyme_forward(semi::SemidiscretizationHyperbolic; N = pick_batchsize(semi))
     # if N == length(x)
     if N == 1
         return vector_mode_jacobian_enzyme_forward(semi)

test/UpwindTest.jl

@@ -27,7 +27,6 @@ end; # 0.326764 seconds (1.09 M allocations: 75.013 MiB, 7.28% gc time, 99.87% c
 @test J1 = J2
 
 @info "Compare the time consumed by Enzyme.jl Δt and ForwardDiff.jl..."
-println("Enzyme: ", Δt₁)
-println("ForwardDiff: ", Δt₂)
+@info "Enzyme: $Δt₁ seconds | ForwardDiff: $Δt₂ seconds."
 
 end