failed to run `@example` block in src/tutorials/L_shape.md:33-53 ```@example L chain = FullyConnected((2,16,16,16,1), tanh) pinn = PINN(chain) sampler = QuasiRandomSampler(300, 30) strategy = NonAdaptiveTraining() prob = Sophon.discretize(pde_system, pinn, sampler, strategy) res = Optimization.solve(prob, BFGS(); maxiters=1000) using CairoMakie xs = -1:0.01:1 ys = -1:0.01:1 u_pred = [ifelse(x>0.0 && y>0.0, NaN, pinn.phi([x,y], res.u)[1]) for x in xs, y in ys] fig, ax, hm = heatmap(xs, ys, u_pred, colormap=:jet) Colorbar(fig[:, end+1], hm) fig save("Lshape.png", fig); nothing # hide ``` exception = ArgumentError: The passed automatic differentiation backend choice is not available. Please load the corresponding AD package Zygote. Stacktrace: [1] instantiate_function(f::Function, x::Optimization.ReInitCache{ComponentArrays.ComponentVector{Float64, Vector{Float64}, Tuple{ComponentArrays.Axis{(layer_1 = ViewAxis(1:48, Axis(weight = ViewAxis(1:32, ShapedAxis((16, 2), NamedTuple())), bias = ViewAxis(33:48, ShapedAxis((16, 1), NamedTuple())))), layer_2 = ViewAxis(49:320, Axis(weight = ViewAxis(1:256, ShapedAxis((16, 16), NamedTuple())), bias = ViewAxis(257:272, ShapedAxis((16, 1), NamedTuple())))), layer_3 = ViewAxis(321:592, Axis(weight = ViewAxis(1:256, ShapedAxis((16, 16), NamedTuple())), bias = ViewAxis(257:272, ShapedAxis((16, 1), NamedTuple())))), layer_4 = ViewAxis(593:609, Axis(weight = ViewAxis(1:16, ShapedAxis((1, 16), NamedTuple())), bias = ViewAxis(17:17, ShapedAxis((1, 1), NamedTuple())))))}}}, Vector{Matrix{Float64}}}, adtype::ADTypes.AutoZygote, p::Int64, num_cons::Int64) @ Optimization ~/.julia/packages/Optimization/fPVIW/src/function.jl:114 [2] instantiate_function(f::Function, x::Optimization.ReInitCache{ComponentArrays.ComponentVector{Float64, Vector{Float64}, Tuple{ComponentArrays.Axis{(layer_1 = ViewAxis(1:48, Axis(weight = ViewAxis(1:32, ShapedAxis((16, 2), NamedTuple())), bias = ViewAxis(33:48, ShapedAxis((16, 1), NamedTuple())))), layer_2 = ViewAxis(49:320, Axis(weight = ViewAxis(1:256, ShapedAxis((16, 16), NamedTuple())), bias = ViewAxis(257:272, ShapedAxis((16, 1), NamedTuple())))), layer_3 = ViewAxis(321:592, Axis(weight = ViewAxis(1:256, ShapedAxis((16, 16), NamedTuple())), bias = ViewAxis(257:272, ShapedAxis((16, 1), NamedTuple())))), layer_4 = ViewAxis(593:609, Axis(weight = ViewAxis(1:16, ShapedAxis((1, 16), NamedTuple())), bias = ViewAxis(17:17, ShapedAxis((1, 1), NamedTuple())))))}}}, Vector{Matrix{Float64}}}, adtype::ADTypes.AutoZygote, p::Int64) @ Optimization ~/.julia/packages/Optimization/fPVIW/src/function.jl:106 [3] Optimization.OptimizationCache(prob::SciMLBase.OptimizationProblem{true, SciMLBase.OptimizationFunction{true, ADTypes.AutoZygote, Sophon.var"#full_loss_function#271"{typeof(Sophon.null_additional_loss), PINN{ChainState{Lux.Chain{NamedTuple{(:layer_1, :layer_2, :layer_3, :layer_4), Tuple{Lux.Dense{true, typeof(tanh), Sophon.var"#75#76"{typeof(tanh)}, typeof(WeightInitializers.zeros32)}, Lux.Dense{true, typeof(tanh), Sophon.var"#75#76"{typeof(tanh)}, typeof(WeightInitializers.zeros32)}, Lux.Dense{true, typeof(tanh), Sophon.var"#75#76"{typeof(tanh)}, typeof(WeightInitializers.zeros32)}, Lux.Dense{true, typeof(identity), Sophon.var"#75#76"{typeof(tanh)}, typeof(WeightInitializers.zeros32)}}}, Nothing}, NamedTuple{(:layer_1, :layer_2, :layer_3, :layer_4), NTuple{4, NamedTuple{(), Tuple{}}}}}, NamedTuple{(:layer_1, :layer_2, :layer_3, :layer_4), NTuple{4, NamedTuple{(:weight, :bias), Tuple{Matrix{Float32}, Matrix{Float32}}}}}}, Sophon.var"#277#278"}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.DEFAULT_OBSERVED_NO_TIME), Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing}, ComponentArrays.ComponentVector{Float64, Vector{Float64}, Tuple{ComponentArrays.Axis{(layer_1 = ViewAxis(1:48, Axis(weight = ViewAxis(1:32, ShapedAxis((16, 2), NamedTuple())), bias = ViewAxis(33:48, ShapedAxis((16, 1), NamedTuple())))), layer_2 = ViewAxis(49:320, Axi

failed to run `@example` block in src/tutorials/SchrödingerEquation.md:42-56 ```@example Schrödinger function train(pde_system, prob, sampler, strategy, resample_period = 500, n=10) bfgs = BFGS() res = Optimization.solve(prob, bfgs; maxiters=2000) for i in 1:n data = Sophon.sample(pde_system, sampler) prob = remake(prob; u0=res.u, p=data) res = Optimization.solve(prob, bfgs; maxiters=resample_period) end return res end res = train(pde_system, prob, sampler, strategy) ``` exception = ArgumentError: The passed automatic differentiation backend choice is not available. Please load the corresponding AD package Zygote. Stacktrace: [1] instantiate_function(f::Function, x::Optimization.ReInitCache{ComponentArrays.ComponentVector{Float64, Vector{Float64}, Tuple{ComponentArrays.Axis{(u = ViewAxis(1:881, Axis(layer_1 = ViewAxis(1:48, Axis(weight = ViewAxis(1:32, ShapedAxis((16, 2), NamedTuple())), bias = ViewAxis(33:48, ShapedAxis((16, 1), NamedTuple())))), layer_2 = ViewAxis(49:320, Axis(weight = ViewAxis(1:256, ShapedAxis((16, 16), NamedTuple())), bias = ViewAxis(257:272, ShapedAxis((16, 1), NamedTuple())))), layer_3 = ViewAxis(321:592, Axis(weight = ViewAxis(1:256, ShapedAxis((16, 16), NamedTuple())), bias = ViewAxis(257:272, ShapedAxis((16, 1), NamedTuple())))), layer_4 = ViewAxis(593:864, Axis(weight = ViewAxis(1:256, ShapedAxis((16, 16), NamedTuple())), bias = ViewAxis(257:272, ShapedAxis((16, 1), NamedTuple())))), layer_5 = ViewAxis(865:881, Axis(weight = ViewAxis(1:16, ShapedAxis((1, 16), NamedTuple())), bias = ViewAxis(17:17, ShapedAxis((1, 1), NamedTuple())))))), v = ViewAxis(882:1762, Axis(layer_1 = ViewAxis(1:48, Axis(weight = ViewAxis(1:32, ShapedAxis((16, 2), NamedTuple())), bias = ViewAxis(33:48, ShapedAxis((16, 1), NamedTuple())))), layer_2 = ViewAxis(49:320, Axis(weight = ViewAxis(1:256, ShapedAxis((16, 16), NamedTuple())), bias = ViewAxis(257:272, ShapedAxis((16, 1), NamedTuple())))), layer_3 = ViewAxis(321:592, Axis(weight = ViewAxis(1:256, ShapedAxis((16, 16), NamedTuple())), bias = ViewAxis(257:272, ShapedAxis((16, 1), NamedTuple())))), layer_4 = ViewAxis(593:864, Axis(weight = ViewAxis(1:256, ShapedAxis((16, 16), NamedTuple())), bias = ViewAxis(257:272, ShapedAxis((16, 1), NamedTuple())))), layer_5 = ViewAxis(865:881, Axis(weight = ViewAxis(1:16, ShapedAxis((1, 16), NamedTuple())), bias = ViewAxis(17:17, ShapedAxis((1, 1), NamedTuple())))))))}}}, Vector{Matrix{Float64}}}, adtype::ADTypes.AutoZygote, p::Int64, num_cons::Int64) @ Optimization ~/.julia/packages/Optimization/fPVIW/src/function.jl:114 [2] instantiate_function(f::Function, x::Optimization.ReInitCache{ComponentArrays.ComponentVector{Float64, Vector{Float64}, Tuple{ComponentArrays.Axis{(u = ViewAxis(1:881, Axis(layer_1 = ViewAxis(1:48, Axis(weight = ViewAxis(1:32, ShapedAxis((16, 2), NamedTuple())), bias = ViewAxis(33:48, ShapedAxis((16, 1), NamedTuple())))), layer_2 = ViewAxis(49:320, Axis(weight = ViewAxis(1:256, ShapedAxis((16, 16), NamedTuple())), bias = ViewAxis(257:272, ShapedAxis((16, 1), NamedTuple())))), layer_3 = ViewAxis(321:592, Axis(weight = ViewAxis(1:256, ShapedAxis((16, 16), NamedTuple())), bias = ViewAxis(257:272, ShapedAxis((16, 1), NamedTuple())))), layer_4 = ViewAxis(593:864, Axis(weight = ViewAxis(1:256, ShapedAxis((16, 16), NamedTuple())), bias = ViewAxis(257:272, ShapedAxis((16, 1), NamedTuple())))), layer_5 = ViewAxis(865:881, Axis(weight = ViewAxis(1:16, ShapedAxis((1, 16), NamedTuple())), bias = ViewAxis(17:17, ShapedAxis((1, 1), NamedTuple())))))), v = ViewAxis(882:1762, Axis(layer_1 = ViewAxis(1:48, Axis(weight = ViewAxis(1:32, ShapedAxis((16, 2), NamedTuple())), bias = ViewAxis(33:48, ShapedAxis((16, 1), NamedTuple())))), layer_2 = ViewAxis(49:320, Axis(weight = ViewAxis(1:256, ShapedAxis((16, 16), NamedTuple())), bias = ViewAxis(257:272, ShapedAxis((16, 1), NamedTuple())))), layer_3 = ViewAxis(321:592, Axis(weight = ViewAxis(1:256, ShapedAxis((16, 16), NamedTuple())), bias = ViewAxis(257:272, ShapedAxis((16, 1), NamedTuple())))), l

failed to run `@example` block in src/tutorials/SchrödingerEquation.md:58-73 ```@example Schrödinger phi = pinn.phi ps = res.u xs, ts= [infimum(d.domain):0.01:supremum(d.domain) for d in pde_system.domain] u = [sum(phi.u(([x,t]), ps.u)) for x in xs, t in ts] v = [sum(phi.v(([x,t]), ps.v)) for x in xs, t in ts] ψ = @. sqrt(u^2+ v^2) axis = (xlabel="t", ylabel="x", title="u") fig, ax1, hm1 = heatmap(ts, xs, u', axis=axis) ax2, hm2= heatmap(fig[1, end+1], ts, xs, v', axis= merge(axis, (; title="v"))) display(fig) save("uv.png", fig); nothing # hide ``` exception = UndefVarError: `res` not defined Stacktrace: [1] top-level scope @ SchrödingerEquation.md:60 [2] eval @ ./boot.jl:370 [inlined] [3] #54 @ ~/.julia/packages/Documenter/nQAq5/src/expander_pipeline.jl:738 [inlined] [4] cd(f::Documenter.var"#54#56"{Module, Expr}, dir::String) @ Base.Filesystem ./file.jl:112 [5] (::Documenter.var"#53#55"{Documenter.Page, Module, Expr})() @ Documenter ~/.julia/packages/Documenter/nQAq5/src/expander_pipeline.jl:737 [6] (::IOCapture.var"#3#5"{DataType, Documenter.var"#53#55"{Documenter.Page, Module, Expr}, Task, IOContext{Base.PipeEndpoint}, IOContext{Base.PipeEndpoint}, IOContext{Base.PipeEndpoint}, IOContext{Base.PipeEndpoint}})() @ IOCapture ~/.julia/packages/IOCapture/8Uj7o/src/IOCapture.jl:119 [7] with_logstate(f::Function, logstate::Any) @ Base.CoreLogging ./logging.jl:514 [8] with_logger @ ./logging.jl:626 [inlined] [9] capture(f::Documenter.var"#53#55"{Documenter.Page, Module, Expr}; rethrow::Type, color::Bool) @ IOCapture ~/.julia/packages/IOCapture/8Uj7o/src/IOCapture.jl:116 [10] runner(#unused#::Type{Documenter.Expanders.ExampleBlocks}, node::MarkdownAST.Node{Nothing}, page::Documenter.Page, doc::Documenter.Document) @ Documenter ~/.julia/packages/Documenter/nQAq5/src/expander_pipeline.jl:736

failed to run `@example` block in src/tutorials/SchrödingerEquation.md:76-82 ```@example Schrödinger axis = (xlabel="t", ylabel="x", title="ψ") fig, ax1, hm1 = heatmap(ts, xs, ψ', axis=axis, colormap=:jet) Colorbar(fig[:, end+1], hm1) display(fig) save("phi.png", fig); nothing # hide ``` exception = UndefVarError: `ψ` not defined Stacktrace: [1] top-level scope @ SchrödingerEquation.md:78 [2] eval @ ./boot.jl:370 [inlined] [3] #54 @ ~/.julia/packages/Documenter/nQAq5/src/expander_pipeline.jl:738 [inlined] [4] cd(f::Documenter.var"#54#56"{Module, Expr}, dir::String) @ Base.Filesystem ./file.jl:112 [5] (::Documenter.var"#53#55"{Documenter.Page, Module, Expr})() @ Documenter ~/.julia/packages/Documenter/nQAq5/src/expander_pipeline.jl:737 [6] (::IOCapture.var"#3#5"{DataType, Documenter.var"#53#55"{Documenter.Page, Module, Expr}, Task, IOContext{Base.PipeEndpoint}, IOContext{Base.PipeEndpoint}, IOContext{Base.PipeEndpoint}, IOContext{Base.PipeEndpoint}})() @ IOCapture ~/.julia/packages/IOCapture/8Uj7o/src/IOCapture.jl:119 [7] with_logstate(f::Function, logstate::Any) @ Base.CoreLogging ./logging.jl:514 [8] with_logger @ ./logging.jl:626 [inlined] [9] capture(f::Documenter.var"#53#55"{Documenter.Page, Module, Expr}; rethrow::Type, color::Bool) @ IOCapture ~/.julia/packages/IOCapture/8Uj7o/src/IOCapture.jl:116 [10] runner(#unused#::Type{Documenter.Expanders.ExampleBlocks}, node::MarkdownAST.Node{Nothing}, page::Documenter.Page, doc::Documenter.Document) @ Documenter ~/.julia/packages/Documenter/nQAq5/src/expander_pipeline.jl:736

failed to run `@example` block in src/tutorials/SchrödingerEquation.md:90-99 ```@example Schrödinger using StatsBase data = vec([[x, t] for x in xs, t in ts]) wv = vec(ψ) new_data = wsample(data, wv, 500) new_data = reduce(hcat, new_data) fig, ax = scatter(new_data[2,:], new_data[1,:]) save("data.png", fig); nothing # hide ``` exception = UndefVarError: `xs` not defined Stacktrace: [1] top-level scope @ SchrödingerEquation.md:93 [2] eval @ ./boot.jl:370 [inlined] [3] #54 @ ~/.julia/packages/Documenter/nQAq5/src/expander_pipeline.jl:738 [inlined] [4] cd(f::Documenter.var"#54#56"{Module, Expr}, dir::String) @ Base.Filesystem ./file.jl:112 [5] (::Documenter.var"#53#55"{Documenter.Page, Module, Expr})() @ Documenter ~/.julia/packages/Documenter/nQAq5/src/expander_pipeline.jl:737 [6] (::IOCapture.var"#3#5"{DataType, Documenter.var"#53#55"{Documenter.Page, Module, Expr}, Task, IOContext{Base.PipeEndpoint}, IOContext{Base.PipeEndpoint}, IOContext{Base.PipeEndpoint}, IOContext{Base.PipeEndpoint}})() @ IOCapture ~/.julia/packages/IOCapture/8Uj7o/src/IOCapture.jl:119 [7] with_logstate(f::Function, logstate::Any) @ Base.CoreLogging ./logging.jl:514 [8] with_logger @ ./logging.jl:626 [inlined] [9] capture(f::Documenter.var"#53#55"{Documenter.Page, Module, Expr}; rethrow::Type, color::Bool) @ IOCapture ~/.julia/packages/IOCapture/8Uj7o/src/IOCapture.jl:116 [10] runner(#unused#::Type{Documenter.Expanders.ExampleBlocks}, node::MarkdownAST.Node{Nothing}, page::Documenter.Page, doc::Documenter.Document) @ Documenter ~/.julia/packages/Documenter/nQAq5/src/expander_pipeline.jl:736

failed to run `@example` block in src/tutorials/SchrödingerEquation.md:102-107 ```@example Schrödinger prob.p[1] = new_data prob.p[2] = new_data prob = remake(prob; u0 = res.u) # res = Optimization.solve(prob, bfgs; maxiters=1000) ``` exception = UndefVarError: `new_data` not defined Stacktrace: [1] top-level scope @ SchrödingerEquation.md:103 [2] eval @ ./boot.jl:370 [inlined] [3] #54 @ ~/.julia/packages/Documenter/nQAq5/src/expander_pipeline.jl:738 [inlined] [4] cd(f::Documenter.var"#54#56"{Module, Expr}, dir::String) @ Base.Filesystem ./file.jl:112 [5] (::Documenter.var"#53#55"{Documenter.Page, Module, Expr})() @ Documenter ~/.julia/packages/Documenter/nQAq5/src/expander_pipeline.jl:737 [6] (::IOCapture.var"#3#5"{DataType, Documenter.var"#53#55"{Documenter.Page, Module, Expr}, Task, IOContext{Base.PipeEndpoint}, IOContext{Base.PipeEndpoint}, IOContext{Base.PipeEndpoint}, IOContext{Base.PipeEndpoint}})() @ IOCapture ~/.julia/packages/IOCapture/8Uj7o/src/IOCapture.jl:119 [7] with_logstate(f::Function, logstate::Any) @ Base.CoreLogging ./logging.jl:514 [8] with_logger @ ./logging.jl:626 [inlined] [9] capture(f::Documenter.var"#53#55"{Documenter.Page, Module, Expr}; rethrow::Type, color::Bool) @ IOCapture ~/.julia/packages/IOCapture/8Uj7o/src/IOCapture.jl:116 [10] runner(#unused#::Type{Documenter.Expanders.ExampleBlocks}, node::MarkdownAST.Node{Nothing}, page::Documenter.Page, doc::Documenter.Document) @ Documenter ~/.julia/packages/Documenter/nQAq5/src/expander_pipeline.jl:736

failed to run `@example` block in src/tutorials/allen_cahn.md:38-53 ```@example allen function train(allen, prob, sampler, strategy) bfgs = BFGS() res = Optimization.solve(prob, bfgs; maxiters=2000) for tmax in [0.5, 0.75, 1.0] allen.domain[2] = t ∈ 0.0..tmax data = Sophon.sample(allen, sampler) prob = remake(prob; u0=res.u, p=data) res = Optimization.solve(prob, bfgs; maxiters=2000) end return res end res = train(allen, prob, sampler, strategy) ``` exception = ArgumentError: The passed automatic differentiation backend choice is not available. Please load the corresponding AD package Zygote. Stacktrace: [1] instantiate_function(f::Function, x::Optimization.ReInitCache{ComponentArrays.ComponentVector{Float64, Vector{Float64}, Tuple{ComponentArrays.Axis{(layer_1 = ViewAxis(1:48, Axis(weight = ViewAxis(1:32, ShapedAxis((16, 2), NamedTuple())), bias = ViewAxis(33:48, ShapedAxis((16, 1), NamedTuple())))), layer_2 = ViewAxis(49:320, Axis(weight = ViewAxis(1:256, ShapedAxis((16, 16), NamedTuple())), bias = ViewAxis(257:272, ShapedAxis((16, 1), NamedTuple())))), layer_3 = ViewAxis(321:592, Axis(weight = ViewAxis(1:256, ShapedAxis((16, 16), NamedTuple())), bias = ViewAxis(257:272, ShapedAxis((16, 1), NamedTuple())))), layer_4 = ViewAxis(593:864, Axis(weight = ViewAxis(1:256, ShapedAxis((16, 16), NamedTuple())), bias = ViewAxis(257:272, ShapedAxis((16, 1), NamedTuple())))), layer_5 = ViewAxis(865:881, Axis(weight = ViewAxis(1:16, ShapedAxis((1, 16), NamedTuple())), bias = ViewAxis(17:17, ShapedAxis((1, 1), NamedTuple())))))}}}, Vector{Matrix{Float64}}}, adtype::ADTypes.AutoZygote, p::Int64, num_cons::Int64) @ Optimization ~/.julia/packages/Optimization/fPVIW/src/function.jl:114 [2] instantiate_function(f::Function, x::Optimization.ReInitCache{ComponentArrays.ComponentVector{Float64, Vector{Float64}, Tuple{ComponentArrays.Axis{(layer_1 = ViewAxis(1:48, Axis(weight = ViewAxis(1:32, ShapedAxis((16, 2), NamedTuple())), bias = ViewAxis(33:48, ShapedAxis((16, 1), NamedTuple())))), layer_2 = ViewAxis(49:320, Axis(weight = ViewAxis(1:256, ShapedAxis((16, 16), NamedTuple())), bias = ViewAxis(257:272, ShapedAxis((16, 1), NamedTuple())))), layer_3 = ViewAxis(321:592, Axis(weight = ViewAxis(1:256, ShapedAxis((16, 16), NamedTuple())), bias = ViewAxis(257:272, ShapedAxis((16, 1), NamedTuple())))), layer_4 = ViewAxis(593:864, Axis(weight = ViewAxis(1:256, ShapedAxis((16, 16), NamedTuple())), bias = ViewAxis(257:272, ShapedAxis((16, 1), NamedTuple())))), layer_5 = ViewAxis(865:881, Axis(weight = ViewAxis(1:16, ShapedAxis((1, 16), NamedTuple())), bias = ViewAxis(17:17, ShapedAxis((1, 1), NamedTuple())))))}}}, Vector{Matrix{Float64}}}, adtype::ADTypes.AutoZygote, p::Int64) @ Optimization ~/.julia/packages/Optimization/fPVIW/src/function.jl:106 [3] Optimization.OptimizationCache(prob::SciMLBase.OptimizationProblem{true, SciMLBase.OptimizationFunction{true, ADTypes.AutoZygote, Sophon.var"#full_loss_function#271"{typeof(Sophon.null_additional_loss), PINN{ChainState{Lux.Chain{NamedTuple{(:layer_1, :layer_2, :layer_3, :layer_4, :layer_5), Tuple{Lux.Dense{true, typeof(tanh), Sophon.var"#75#76"{typeof(tanh)}, typeof(WeightInitializers.zeros32)}, Lux.Dense{true, typeof(tanh), Sophon.var"#75#76"{typeof(tanh)}, typeof(WeightInitializers.zeros32)}, Lux.Dense{true, typeof(tanh), Sophon.var"#75#76"{typeof(tanh)}, typeof(WeightInitializers.zeros32)}, Lux.Dense{true, typeof(tanh), Sophon.var"#75#76"{typeof(tanh)}, typeof(WeightInitializers.zeros32)}, Lux.Dense{true, typeof(identity), Sophon.var"#75#76"{typeof(tanh)}, typeof(WeightInitializers.zeros32)}}}, Nothing}, NamedTuple{(:layer_1, :layer_2, :layer_3, :layer_4, :layer_5), NTuple{5, NamedTuple{(), Tuple{}}}}}, NamedTuple{(:layer_1, :layer_2, :layer_3, :layer_4, :layer_5), NTuple{5, NamedTuple{(:weight, :bias), Tuple{Matrix{Float32}, Matrix{Float32}}}}}}, Sophon.var"#281#282"}, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, typeof(SciMLBase.D

failed to run `@example` block in src/tutorials/allen_cahn.md:56-66 ```@example allen using CairoMakie phi = pinn.phi xs, ts = [infimum(d.domain):0.01:supremum(d.domain) for d in allen.domain] axis = (xlabel="t", ylabel="x", title="Prediction") u_pred = [sum(pinn.phi([x, t], res.u)) for x in xs, t in ts] fig, ax, hm = heatmap(ts, xs, u_pred', axis=axis) save("allen.png", fig); nothing # hide ``` exception = UndefVarError: `res` not defined Stacktrace: [1] (::Main.__atexample__named__allen.var"#3#4")(::Tuple{Float64, Float64}) @ Main.__atexample__named__allen ./none:0 [2] iterate @ ./generator.jl:47 [inlined] [3] collect(itr::Base.Generator{Base.Iterators.ProductIterator{Tuple{StepRangeLen{Float64, Base.TwicePrecision{Float64}, Base.TwicePrecision{Float64}, Int64}, StepRangeLen{Float64, Base.TwicePrecision{Float64}, Base.TwicePrecision{Float64}, Int64}}}, Main.__atexample__named__allen.var"#3#4"}) @ Base ./array.jl:782 [4] top-level scope @ allen_cahn.md:62 [5] eval @ ./boot.jl:370 [inlined] [6] #54 @ ~/.julia/packages/Documenter/nQAq5/src/expander_pipeline.jl:738 [inlined] [7] cd(f::Documenter.var"#54#56"{Module, Expr}, dir::String) @ Base.Filesystem ./file.jl:112 [8] (::Documenter.var"#53#55"{Documenter.Page, Module, Expr})() @ Documenter ~/.julia/packages/Documenter/nQAq5/src/expander_pipeline.jl:737 [9] (::IOCapture.var"#3#5"{DataType, Documenter.var"#53#55"{Documenter.Page, Module, Expr}, Task, IOContext{Base.PipeEndpoint}, IOContext{Base.PipeEndpoint}, IOContext{Base.PipeEndpoint}, IOContext{Base.PipeEndpoint}})() @ IOCapture ~/.julia/packages/IOCapture/8Uj7o/src/IOCapture.jl:119 [10] with_logstate(f::Function, logstate::Any) @ Base.CoreLogging ./logging.jl:514 [11] with_logger @ ./logging.jl:626 [inlined] [12] capture(f::Documenter.var"#53#55"{Documenter.Page, Module, Expr}; rethrow::Type, color::Bool) @ IOCapture ~/.julia/packages/IOCapture/8Uj7o/src/IOCapture.jl:116 [13] runner(#unused#::Type{Documenter.Expanders.ExampleBlocks}, node::MarkdownAST.Node{Nothing}, page::Documenter.Page, doc::Documenter.Document) @ Documenter ~/.julia/packages/Documenter/nQAq5/src/expander_pipeline.jl:736

failed to run `@example` block in src/tutorials/convection.md:43-51 ```@example convection sampler = QuasiRandomSampler(500, 100) # data points strategy = NonAdaptiveTraining(1 , 500) # weights pinn = PINN(chain) prob = Sophon.discretize(convection, pinn, sampler, strategy) @time res = Optimization.solve(prob, BFGS(); maxiters = 1000) ``` exception = ArgumentError: The passed automatic differentiation backend choice is not available. Please load the corresponding AD package Zygote. Stacktrace: [1] instantiate_function(f::Function, x::Optimization.ReInitCache{ComponentArrays.ComponentVector{Float64, Vector{Float64}, Tuple{ComponentArrays.Axis{(filters = ViewAxis(1:128, Axis(filter_1 = ViewAxis(1:32, Axis(bias = ViewAxis(1:32, ShapedAxis((32, 1), NamedTuple())),)), filter_2 = ViewAxis(33:64, Axis(bias = ViewAxis(1:32, ShapedAxis((32, 1), NamedTuple())),)), filter_3 = ViewAxis(65:96, Axis(bias = ViewAxis(1:32, ShapedAxis((32, 1), NamedTuple())),)), filter_4 = ViewAxis(97:128, Axis(bias = ViewAxis(1:32, ShapedAxis((32, 1), NamedTuple())),)))), linear_layers = ViewAxis(129:3296, Axis(layer_1 = ViewAxis(1:1056, Axis(weight = ViewAxis(1:1024, ShapedAxis((32, 32), NamedTuple())), bias = ViewAxis(1025:1056, ShapedAxis((32, 1), NamedTuple())))), layer_2 = ViewAxis(1057:2112, Axis(weight = ViewAxis(1:1024, ShapedAxis((32, 32), NamedTuple())), bias = ViewAxis(1025:1056, ShapedAxis((32, 1), NamedTuple())))), layer_3 = ViewAxis(2113:3168, Axis(weight = ViewAxis(1:1024, ShapedAxis((32, 32), NamedTuple())), bias = ViewAxis(1025:1056, ShapedAxis((32, 1), NamedTuple())))))), output_layer = ViewAxis(3297:3329, Axis(weight = ViewAxis(1:32, ShapedAxis((1, 32), NamedTuple())), bias = ViewAxis(33:33, ShapedAxis((1, 1), NamedTuple())))))}}}, Vector{Matrix{Float64}}}, adtype::ADTypes.AutoZygote, p::Int64, num_cons::Int64) @ Optimization ~/.julia/packages/Optimization/fPVIW/src/function.jl:114 [2] instantiate_function(f::Function, x::Optimization.ReInitCache{ComponentArrays.ComponentVector{Float64, Vector{Float64}, Tuple{ComponentArrays.Axis{(filters = ViewAxis(1:128, Axis(filter_1 = ViewAxis(1:32, Axis(bias = ViewAxis(1:32, ShapedAxis((32, 1), NamedTuple())),)), filter_2 = ViewAxis(33:64, Axis(bias = ViewAxis(1:32, ShapedAxis((32, 1), NamedTuple())),)), filter_3 = ViewAxis(65:96, Axis(bias = ViewAxis(1:32, ShapedAxis((32, 1), NamedTuple())),)), filter_4 = ViewAxis(97:128, Axis(bias = ViewAxis(1:32, ShapedAxis((32, 1), NamedTuple())),)))), linear_layers = ViewAxis(129:3296, Axis(layer_1 = ViewAxis(1:1056, Axis(weight = ViewAxis(1:1024, ShapedAxis((32, 32), NamedTuple())), bias = ViewAxis(1025:1056, ShapedAxis((32, 1), NamedTuple())))), layer_2 = ViewAxis(1057:2112, Axis(weight = ViewAxis(1:1024, ShapedAxis((32, 32), NamedTuple())), bias = ViewAxis(1025:1056, ShapedAxis((32, 1), NamedTuple())))), layer_3 = ViewAxis(2113:3168, Axis(weight = ViewAxis(1:1024, ShapedAxis((32, 32), NamedTuple())), bias = ViewAxis(1025:1056, ShapedAxis((32, 1), NamedTuple())))))), output_layer = ViewAxis(3297:3329, Axis(weight = ViewAxis(1:32, ShapedAxis((1, 32), NamedTuple())), bias = ViewAxis(33:33, ShapedAxis((1, 1), NamedTuple())))))}}}, Vector{Matrix{Float64}}}, adtype::ADTypes.AutoZygote, p::Int64) @ Optimization ~/.julia/packages/Optimization/fPVIW/src/function.jl:106 [3] Optimization.OptimizationCache(prob::SciMLBase.OptimizationProblem{true, SciMLBase.OptimizationFunction{true, ADTypes.AutoZygote, Sophon.var"#full_loss_function#271"{typeof(Sophon.null_additional_loss), PINN{ChainState{Sophon.MultiplicativeFilterNet{Lux.BranchLayer{NamedTuple{(:filter_1, :filter_2, :filter_3, :filter_4), NTuple{4, DiscreteFourierFeature{Int64, Int64}}}, Union{Nothing, String, Symbol}}, Lux.PairwiseFusion{NamedTuple{(:layer_1, :layer_2, :layer_3), Tuple{Lux.Dense{true, typeof(identity), Sophon.var"#75#76"{typeof(sin)}, typeof(WeightInitializers.zeros32)}, Lux.Dense{true, typeof(identity), Sophon.var"#75#76"{typeof(sin)}, typeof(WeightInitializers.zeros32)}, Lux.Dense{true, typeof(identity), Sophon.var"#75#76"{typeof(sin)}, typeof(W