Update documentation build to Julia 1.11.

Ferrite-FEM · Nov 26, 2024 · b6ae45c · b6ae45c
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diff --git a/docs/Manifest.toml b/docs/Manifest.toml
diff --git a/docs/Project.toml b/docs/Project.toml
@@ -1,3 +1,5 @@
 [deps]
 Documenter = "e30172f5-a6a5-5a46-863b-614d45cd2de4"
+ForwardDiff = "f6369f11-7733-5829-9624-2563aa707210"
+StaticArrays = "90137ffa-7385-5640-81b9-e52037218182"
 Tensors = "48a634ad-e948-5137-8d70-aa71f2a747f4"
diff --git a/docs/src/demos.md b/docs/src/demos.md
@@ -129,13 +129,13 @@ julia> C = tdot(F);
 
 julia> S_AD = 2 * gradient(C -> Ψ(C, μ, Kb), C)
 3×3 SymmetricTensor{2, 3, Float64, 6}:
-  4.30534e11  -2.30282e11  -8.52861e10
- -2.30282e11   4.38793e11  -2.64481e11
- -8.52861e10  -2.64481e11   7.85515e11
+  2.36415e11  -1.37206e11  -3.15432e10
+ -1.37206e11   2.18256e11  -6.70562e10
+ -3.15432e10  -6.70562e10   1.06713e11
 
 julia> S(C, μ, Kb)
 3×3 SymmetricTensor{2, 3, Float64, 6}:
-  4.30534e11  -2.30282e11  -8.52861e10
- -2.30282e11   4.38793e11  -2.64481e11
- -8.52861e10  -2.64481e11   7.85515e11
+  2.36415e11  -1.37206e11  -3.15432e10
+ -1.37206e11   2.18256e11  -6.70562e10
+ -3.15432e10  -6.70562e10   1.06713e11
 ```
diff --git a/docs/src/man/automatic_differentiation.md b/docs/src/man/automatic_differentiation.md
@@ -45,13 +45,13 @@ julia> x = rand(Vec{2});
 
 julia> gradient(norm, x)
 2-element Vec{2, Float64}:
- 0.6103600560550116
- 0.7921241076829584
+ 0.5105128363207563
+ 0.859870132026771
 
 julia> x / norm(x)
 2-element Vec{2, Float64}:
- 0.6103600560550116
- 0.7921241076829584
+ 0.5105128363207563
+ 0.8598701320267711
 ```
 
 ### Determinant of a second order symmetric tensor
@@ -65,13 +65,13 @@ julia> A = rand(SymmetricTensor{2,2});
 
 julia> gradient(det, A)
 2×2 SymmetricTensor{2, 2, Float64, 3}:
-  0.566237  -0.766797
- -0.766797   0.590845
+  0.218587  -0.549051
+ -0.549051   0.325977
 
 julia> inv(A)' * det(A)
 2×2 SymmetricTensor{2, 2, Float64, 3}:
-  0.566237  -0.766797
- -0.766797   0.590845
+  0.218587  -0.549051
+ -0.549051   0.325977
 ```
 
 ### Hessian of a quadratic potential

diff --git a/docs/src/man/constructing_tensors.md b/docs/src/man/constructing_tensors.md
@@ -91,19 +91,19 @@ A tensor with random numbers is created using the function `rand`, applied to th
 ```jldoctest
 julia> rand(Tensor{2, 3})
 3×3 Tensor{2, 3, Float64, 9}:
- 0.590845  0.460085  0.200586
- 0.766797  0.794026  0.298614
- 0.566237  0.854147  0.246837
+ 0.325977  0.894245  0.953125
+ 0.549051  0.353112  0.795547
+ 0.218587  0.394255  0.49425
 ```
 
 By specifying the type, `T`, a tensor of different type can be obtained:
 
 ```jldoctest
 julia> rand(SymmetricTensor{2,3,Float32})
 3×3 SymmetricTensor{2, 3, Float32, 6}:
- 0.0107703  0.305865  0.2082
- 0.305865   0.405684  0.257278
- 0.2082     0.257278  0.958491
+ 0.325977  0.549051  0.218587
+ 0.549051  0.894245  0.353112
+ 0.218587  0.353112  0.394255
 ```
 
 ## [Identity tensors](@id identity_tensors)
@@ -202,23 +202,23 @@ following code
 ```jldoctest fromarray
 julia> data = rand(2, 5)
 2×5 Matrix{Float64}:
- 0.590845  0.566237  0.794026  0.200586  0.246837
- 0.766797  0.460085  0.854147  0.298614  0.579672
+ 0.579862  0.972136   0.520355  0.839622  0.131026
+ 0.411294  0.0149088  0.639562  0.967143  0.946453
 
 julia> tensor_data = reinterpret(Vec{2, Float64}, vec(data))
 5-element reinterpret(Vec{2, Float64}, ::Vector{Float64}):
- [0.5908446386657102, 0.7667970365022592]
- [0.5662374165061859, 0.4600853424625171]
- [0.7940257103317943, 0.8541465903790502]
- [0.20058603493384108, 0.2986142783434118]
- [0.24683718661000897, 0.5796722333690416]
+ [0.5798621201341324, 0.4112941179498505]
+ [0.9721360824554687, 0.014908849285099945]
+ [0.520354993723718, 0.6395615996802734]
+ [0.8396219340580711, 0.967142768915383]
+ [0.13102565622085904, 0.9464532262313834]
 ```
 
 The data can also be reinterpreted back to a Julia `Array`
 
 ```jldoctest fromarray
 julia> data = reshape(reinterpret(Float64, tensor_data), (2, 5))
 2×5 Matrix{Float64}:
- 0.590845  0.566237  0.794026  0.200586  0.246837
- 0.766797  0.460085  0.854147  0.298614  0.579672
+ 0.579862  0.972136   0.520355  0.839622  0.131026
+ 0.411294  0.0149088  0.639562  0.967143  0.946453
 ```
diff --git a/docs/src/man/indexing.md b/docs/src/man/indexing.md
@@ -14,12 +14,12 @@ Indexing into a `(Symmetric)Tensor{dim, order}` is performed like for an `Array`
 julia> A = rand(Tensor{2, 2});
 
 julia> A[1, 2]
-0.5662374165061859
+0.21858665481883066
 
 julia> B = rand(SymmetricTensor{4, 2});
 
 julia> B[1, 2, 1, 2]
-0.24683718661000897
+0.4942498668904206
 ```
 
 Slicing will produce a `Tensor` of lower order.
@@ -29,8 +29,8 @@ julia> A = rand(Tensor{2, 2});
 
 julia> A[:, 1]
 2-element Vec{2, Float64}:
- 0.5908446386657102
- 0.7667970365022592
+ 0.32597672886359486
+ 0.5490511363155669
 ```
 
 Since `Tensor`s are immutable there is no `setindex!` function defined on them. Instead, use the functionality to create tensors from functions as described [here](@ref function_index). As an example, this sets the `[1,2]` index on a tensor to one and the rest to zero:

diff --git a/docs/src/man/other_operators.md b/docs/src/man/other_operators.md
@@ -259,21 +259,25 @@ illustrated by the following example, this will give the correct result. In gene
 however, direct differentiation of `Tensor`s is faster (see
 [Automatic Differentiation](@ref)).
 
-```jldoctest
+```jldoctest ad-voigt
 julia> using Tensors, ForwardDiff
 
 julia> fun(X::SymmetricTensor{2}) = X;
 
 julia> A = rand(SymmetricTensor{2,2});
+```
 
-# Differentiation of a tensor directly (correct)
+Differentiation of a tensor directly (correct):
+```jldoctest ad-voigt
 julia> tovoigt(gradient(fun, A))
 3×3 Matrix{Float64}:
  1.0  0.0  0.0
  0.0  1.0  0.0
  0.0  0.0  0.5
+```
 
-# Converting to Voigt format, perform differentiation, convert back (WRONG!)
+Converting to Voigt format, perform differentiation, convert back (WRONG!):
+```jldoctest ad-voigt
 julia> ForwardDiff.jacobian(
            v -> tovoigt(fun(fromvoigt(SymmetricTensor{2,2}, v))),
            tovoigt(A)
@@ -282,8 +286,10 @@ julia> ForwardDiff.jacobian(
  1.0  0.0  0.0
  0.0  1.0  0.0
  0.0  0.0  1.0
+```
 
-# Converting to Mandel format, perform differentiation, convert back (correct)
+Converting to Mandel format, perform differentiation, convert back (correct)
+```jldoctest ad-voigt
 julia> tovoigt(
            frommandel(SymmetricTensor{4,2},
                 ForwardDiff.jacobian(

diff --git a/src/automatic_differentiation.jl b/src/automatic_differentiation.jl
@@ -474,8 +474,8 @@ julia> A = rand(SymmetricTensor{2, 2});
 
 julia> ∇f = gradient(norm, A)
 2×2 SymmetricTensor{2, 2, Float64, 3}:
- 0.434906  0.56442
- 0.56442   0.416793
+ 0.374672  0.63107
+ 0.63107   0.25124
 
 julia> ∇f, f = gradient(norm, A, :all);
 ```
@@ -507,20 +507,20 @@ julia> A = rand(SymmetricTensor{2, 2});
 julia> ∇∇f = hessian(norm, A)
 2×2×2×2 SymmetricTensor{4, 2, Float64, 9}:
 [:, :, 1, 1] =
-  0.596851  -0.180684
- -0.180684  -0.133425
+  0.988034  -0.271765
+ -0.271765  -0.108194
 
 [:, :, 2, 1] =
- -0.180684   0.133546
-  0.133546  -0.173159
+ -0.271765   0.11695
+  0.11695   -0.182235
 
 [:, :, 1, 2] =
- -0.180684   0.133546
-  0.133546  -0.173159
+ -0.271765   0.11695
+  0.11695   -0.182235
 
 [:, :, 2, 2] =
- -0.133425  -0.173159
- -0.173159   0.608207
+ -0.108194  -0.182235
+ -0.182235   1.07683
 
 julia> ∇∇f, ∇f, f = hessian(norm, A, :all);
 ```
@@ -594,15 +594,15 @@ julia> x = rand(Vec{3});
 julia> f(x) = norm(x);
 
 julia> laplace(f, x)
-1.7833701103136868
+2.9633756571179273
 
 julia> g(x) = x*norm(x);
 
 julia> laplace.(g, x)
 3-element Vec{3, Float64}:
- 2.107389336871036
- 2.7349658311504834
- 2.019621767876747
+ 1.9319830062026155
+ 3.2540895437409754
+ 1.2955087437219237
 ```
 """
 function laplace(f::F, v) where F

diff --git a/src/eigen.jl b/src/eigen.jl
@@ -50,13 +50,13 @@ julia> E = eigen(A);
 
 julia> E.values
 2-element Vec{2, Float64}:
- -0.1883547111127678
-  1.345436766284664
+ -0.27938877799585415
+  0.8239521616782797
 
 julia> E.vectors
 2×2 Tensor{2, 2, Float64, 4}:
- -0.701412  0.712756
-  0.712756  0.701412
+ -0.671814  0.74072
+  0.74072   0.671814
 ```
 """
 LinearAlgebra.eigen(::SymmetricTensor{2})

diff --git a/src/math_ops.jl b/src/math_ops.jl
@@ -10,12 +10,12 @@ Computes the norm of a tensor.
 ```jldoctest
 julia> A = rand(Tensor{2,3})
 3×3 Tensor{2, 3, Float64, 9}:
- 0.590845  0.460085  0.200586
- 0.766797  0.794026  0.298614
- 0.566237  0.854147  0.246837
+ 0.325977  0.894245  0.953125
+ 0.549051  0.353112  0.795547
+ 0.218587  0.394255  0.49425
 
 julia> norm(A)
-1.7377443667834922
+1.8223398556552728
 ```
 """
 @inline LinearAlgebra.norm(v::Vec) = sqrt(dot(v, v))
@@ -61,12 +61,12 @@ Computes the determinant of a second order tensor.
 ```jldoctest
 julia> A = rand(SymmetricTensor{2,3})
 3×3 SymmetricTensor{2, 3, Float64, 6}:
- 0.590845  0.766797  0.566237
- 0.766797  0.460085  0.794026
- 0.566237  0.794026  0.854147
+ 0.325977  0.549051  0.218587
+ 0.549051  0.894245  0.353112
+ 0.218587  0.353112  0.394255
 
 julia> det(A)
--0.1005427219925894
+-0.002539324113350679
 ```
 """
 @inline LinearAlgebra.det(t::SecondOrderTensor{1}) = @inbounds t[1,1]
@@ -86,15 +86,15 @@ Computes the inverse of a second order tensor.
 ```jldoctest
 julia> A = rand(Tensor{2,3})
 3×3 Tensor{2, 3, Float64, 9}:
- 0.590845  0.460085  0.200586
- 0.766797  0.794026  0.298614
- 0.566237  0.854147  0.246837
+ 0.325977  0.894245  0.953125
+ 0.549051  0.353112  0.795547
+ 0.218587  0.394255  0.49425
 
 julia> inv(A)
 3×3 Tensor{2, 3, Float64, 9}:
-  19.7146   -19.2802    7.30384
-   6.73809  -10.7687    7.55198
- -68.541     81.4917  -38.8361
+ -587.685  -279.668   1583.46
+ -411.743  -199.494   1115.12
+  588.35    282.819  -1587.79
 ```
 """
 @generated function Base.inv(t::Tensor{2, dim}) where {dim}
@@ -191,13 +191,13 @@ second order tensor `S`, such that `√S ⋅ √S == S`.
 ```jldoctest
 julia> S = rand(SymmetricTensor{2,2}); S = tdot(S)
 2×2 SymmetricTensor{2, 2, Float64, 3}:
- 0.937075  0.887247
- 0.887247  0.908603
+ 0.407718  0.298993
+ 0.298993  0.349237
 
 julia> sqrt(S)
 2×2 SymmetricTensor{2, 2, Float64, 3}:
- 0.776178  0.578467
- 0.578467  0.757614
+ 0.578172  0.270989
+ 0.270989  0.525169
 
 julia> √S ⋅ √S ≈ S
 true
@@ -232,12 +232,12 @@ Computes the trace of a second order tensor.
 ```jldoctest
 julia> A = rand(SymmetricTensor{2,3})
 3×3 SymmetricTensor{2, 3, Float64, 6}:
- 0.590845  0.766797  0.566237
- 0.766797  0.460085  0.794026
- 0.566237  0.794026  0.854147
+ 0.325977  0.549051  0.218587
+ 0.549051  0.894245  0.353112
+ 0.218587  0.353112  0.394255
 
 julia> tr(A)
-1.9050765715072775
+1.6144775244804341
 ```
 """
 @generated function LinearAlgebra.tr(S::SecondOrderTensor{dim}) where {dim}
@@ -259,15 +259,15 @@ based on the additive decomposition.
 ```jldoctest
 julia> A = rand(SymmetricTensor{2,3})
 3×3 SymmetricTensor{2, 3, Float64, 6}:
- 0.590845  0.766797  0.566237
- 0.766797  0.460085  0.794026
- 0.566237  0.794026  0.854147
+ 0.325977  0.549051  0.218587
+ 0.549051  0.894245  0.353112
+ 0.218587  0.353112  0.394255
 
 julia> vol(A)
 3×3 SymmetricTensor{2, 3, Float64, 6}:
- 0.635026  0.0       0.0
- 0.0       0.635026  0.0
- 0.0       0.0       0.635026
+ 0.538159  0.0       0.0
+ 0.0       0.538159  0.0
+ 0.0       0.0       0.538159
 
 julia> vol(A) + dev(A) ≈ A
 true
@@ -286,12 +286,12 @@ julia> A = rand(Tensor{2, 3});
 
 julia> dev(A)
 3×3 Tensor{2, 3, Float64, 9}:
- 0.0469421  0.460085   0.200586
- 0.766797   0.250123   0.298614
- 0.566237   0.854147  -0.297065
+ -0.065136   0.894245   0.953125
+  0.549051  -0.0380011  0.795547
+  0.218587   0.394255   0.103137
 
 julia> tr(dev(A))
-0.0
+5.551115123125783e-17
 ```
 """
 @inline function dev(S::SecondOrderTensor)