PINNErrorVsTime Benchmark Updates #1159

ParamThakkar123 · 2025-01-18T13:22:50Z

Checklist

Appropriate tests were added
Any code changes were done in a way that does not break public API
All documentation related to code changes were updated
The new code follows the
contributor guidelines, in particular the SciML Style Guide and
COLPRAC.
Any new documentation only uses public API

Additional context

Add any other context about the problem here.

ParamThakkar123 · 2025-01-19T06:18:05Z

@ChrisRackauckas I got an error on running the iterations which said that the maxiters are less than 1000 so I set all maxiters to 1100. Actually the decision was a bit arbitrary but is that a good number ??

ChrisRackauckas · 2025-01-19T07:01:24Z

https://docs.sciml.ai/SciMLBenchmarksOutput/v0.5/PINNErrorsVsTime/diffusion_et/

It's supposed to just show the error over time and then get cutoff. I don't see why making it longer would help.

ParamThakkar123 · 2025-01-19T07:02:37Z

https://docs.sciml.ai/SciMLBenchmarksOutput/v0.5/PINNErrorsVsTime/diffusion_et/

It's supposed to just show the error over time and then get cutoff. I don't see why making it longer would help.

Yes. Actually I set it to that number just to get rid of that error

ChrisRackauckas · 2025-01-19T07:06:04Z

Wait what's the error?

ParamThakkar123 · 2025-01-19T07:32:21Z

Wait what's the error?

the error went off on running again

ChrisRackauckas · 2025-01-19T07:36:06Z

what error?

ParamThakkar123 · 2025-01-19T07:36:35Z

maxiters should be a number greater than 1000

ChrisRackauckas · 2025-01-19T07:37:26Z

can you please just show the error...

ParamThakkar123 · 2025-01-19T07:41:22Z

AssertionError: maxiters for CubaCuhre(0, 0, 0) should be larger than 1000

Stacktrace: [1] __solvebp_call(prob::IntegralProblem{false, ComponentArrays.ComponentVector{Float64, Vector{Float64}, Tuple{ComponentArrays.Axis{(layer_1 = ViewAxis(1:60, Axis(weight = ViewAxis(1:50, ShapedAxis((10, 5))), bias = ViewAxis(51:60, ShapedAxis((10, 1))))), layer_2 = ViewAxis(61:170, Axis(weight = ViewAxis(1:100, ShapedAxis((10, 10))), bias = ViewAxis(101:110, ShapedAxis((10, 1))))), layer_3 = ViewAxis(171:181, Axis(weight = ViewAxis(1:10, ShapedAxis((1, 10))), bias = ViewAxis(11:11, ShapedAxis((1, 1))))))}}}, NeuralPDE.var"#integrand#109"{NeuralPDE.var"#219#220"{RuntimeGeneratedFunctions.RuntimeGeneratedFunction{(:cord, Symbol("##θ#228"), :phi, :derivative, :integral, :u, :p),
NeuralPDE.var"#_RGF_ModTag", NeuralPDE.var"#_RGF_ModTag", (0xd9696f1d, 0xe356e73c, 0x32906e9c, 0x54a064bc, 0x0cbbe458), Expr}, NeuralPDE.var"#12#13", NeuralPDE.var"#279#286"{NeuralPDE.var"#279#280#287"{typeof(NeuralPDE.numeric_derivative)}, Dict{Symbol, Int64}, Dict{Symbol, Int64}, QuadratureTraining{CubaCuhre, Float64}}, typeof(NeuralPDE.numeric_derivative), NeuralPDE.Phi{Chain{@NamedTuple{layer_1::Dense{true, typeof(tanh_fast), typeof(glorot_uniform), typeof(zeros32)}, layer_2::Dense{true, typeof(tanh_fast), typeof(glorot_uniform), typeof(zeros32)}, layer_3::Dense{true, typeof(identity), typeof(glorot_uniform), typeof(zeros32)}}, Nothing}, @NamedTuple{layer_1::@NamedTuple{}, layer_2::@NamedTuple{}, layer_3::@NamedTuple{}}}, Nothing}}, Vector{Float64}, @Kwargs{}}, alg::CubaCuhre,
sensealg::Integrals.ReCallVJP{Integrals.ZygoteVJP}, lb::Vector{Float64}, ub::Vector{Float64}, p::ComponentArrays.ComponentVector{Float64, Vector{Float64}, Tuple{ComponentArrays.Axis{(layer_1 = ViewAxis(1:60, Axis(weight = ViewAxis(1:50, ShapedAxis((10, 5))), bias = ViewAxis(51:60, ShapedAxis((10, 1))))), layer_2 = ViewAxis(61:170, Axis(weight = ViewAxis(1:100, ShapedAxis((10, 10))), bias = ViewAxis(101:110, ShapedAxis((10, 1))))), layer_3 = ViewAxis(171:181, Axis(weight = ViewAxis(1:10, ShapedAxis((1, 10))), bias = ViewAxis(11:11, ShapedAxis((1, 1))))))}}}; reltol::Float64, abstol::Float64, maxiters::Int64) @ IntegralsCuba C:\Users\Hp.julia\packages\IntegralsCuba\xueKH\src\IntegralsCuba.jl:139 [2] __solvebp_call @ C:\Users\Hp.julia\packages\IntegralsCuba\xueKH\src\IntegralsCuba.jl:134 [inlined] [3] #__solvebp_call#4 @ C:\Users\Hp.julia\packages\Integrals\d3rQd\src\common.jl:95 [inlined] [4] __solvebp_call @ C:\Users\Hp.julia\packages\Integrals\d3rQd\src\common.jl:94 [inlined] [5] #rrule#5 @ C:\Users\Hp.julia\packages\Integrals\d3rQd\ext\IntegralsZygoteExt.jl:17 [inlined] [6] rrule @ C:\Users\Hp.julia\packages\Integrals\d3rQd\ext\IntegralsZygoteExt.jl:14 [inlined] [7] rrule @ C:\Users\Hp.julia\packages\ChainRulesCore\U6wNx\src\rules.jl:144 [inlined] [8] chain_rrule_kw @ C:\Users\Hp.julia\packages\Zygote\TWpme\src\compiler\chainrules.jl:236 [inlined] [9] macro expansion @ C:\Users\Hp.julia\packages\Zygote\TWpme\src\compiler\interface2.jl:0 [inlined] [10] _pullback @ C:\Users\Hp.julia\packages\Zygote\TWpme\src\compiler\interface2.jl:91 [inlined] [11] solve! @ C:\Users\Hp.julia\packages\Integrals\d3rQd\src\common.jl:84 [inlined] ... @ SciMLBase C:\Users\Hp.julia\packages\SciMLBase\szsYq\src\solve.jl:162 [55] solve(::OptimizationProblem{true, OptimizationFunction{true, AutoZygote, NeuralPDE.var"#full_loss_function#318"{NeuralPDE.var"#null_nonadaptive_loss#118", Vector{NeuralPDE.var"#106#110"{NeuralPDE.var"#219#220"{RuntimeGeneratedFunctions.RuntimeGeneratedFunction{(:cord, Symbol("##θ#228"), :phi, :derivative, :integral, :u, :p), NeuralPDE.var"#_RGF_ModTag", NeuralPDE.var"#_RGF_ModTag", (0x0ca831bf, 0x51abc8eb, 0xda1f388f, 0xf472bcea, 0x7492cfcb), Expr}, NeuralPDE.var"#12#13", NeuralPDE.var"#279#286"{NeuralPDE.var"#279#280#287"{typeof(NeuralPDE.numeric_derivative)}, Dict{Symbol, Int64}, Dict{Symbol, Int64}, QuadratureTraining{CubaCuhre, Float64}}, typeof(NeuralPDE.numeric_derivative), NeuralPDE.Phi{Chain{@NamedTuple{layer_1::Dense{true, typeof(tanh_fast), typeof(glorot_uniform), typeof(zeros32)}, layer_2::Dense{true, typeof(tanh_fast), typeof(glorot_uniform), typeof(zeros32)}, layer_3::Dense{true, typeof(identity), typeof(glorot_uniform), typeof(zeros32)}}, Nothing}, @NamedTuple{layer_1::@NamedTuple{}, layer_2::@NamedTuple{}, layer_3::@NamedTuple{}}}, Nothing}, Vector{Float64}, Vector{Float64}, NeuralPDE.var"#105#108"{QuadratureTraining{CubaCuhre, Float64}}, Float64}}, Vector{NeuralPDE.var"#106#110"{NeuralPDE.var"#219#220"{RuntimeGeneratedFunctions.RuntimeGeneratedFunction{(:cord, Symbol("##θ#228"), :phi, :derivative, :integral, :u, :p), NeuralPDE.var"#_RGF_ModTag", NeuralPDE.var"#RGF_ModTag", (0xd9696f1d, 0xe356e73c, 0x32906e9c, 0x54a064bc, 0x0cbbe458), Expr}, NeuralPDE.var"#12#13", NeuralPDE.var"#279#286"{NeuralPDE.var"#279#280#287"{typeof(NeuralPDE.numeric_derivative)}, Dict{Symbol, Int64}, Dict{Symbol, Int64}, QuadratureTraining{CubaCuhre, Float64}}, typeof(NeuralPDE.numeric_derivative), NeuralPDE.Phi{Chain{@NamedTuple{layer_1::Dense{true, typeof(tanh_fast), typeof(glorot_uniform), typeof(zeros32)}, layer_2::Dense{true, typeof(tanh_fast), typeof(glorot_uniform), typeof(zeros32)}, layer_3::Dense{true, typeof(identity), typeof(glorot_uniform), typeof(zeros32)}}, Nothing}, @NamedTuple{layer_1::@NamedTuple{}, layer_2::@NamedTuple{}, layer_3::@NamedTuple{}}}, Nothing}, Vector{Float64}, Vector{Float64}, NeuralPDE.var"#105#108"{QuadratureTraining{CubaCuhre, Float64}}, Float64}}, NeuralPDE.PINNRepresentation, Bool, Vector{Int64}, Int64, NeuralPDE.Phi{Chain{@NamedTuple{layer_1::Dense{true, typeof(tanh_fast), typeof(glorot_uniform), typeof(zeros32)}, layer_2::Dense{true, typeof(tanh_fast), typeof(glorot_uniform), typeof(zeros32)}, layer_3::Dense{true, typeof(identity), typeof(glorot_uniform), typeof(zeros32)}}, Nothing}, @NamedTuple{layer_1::@NamedTuple{}, layer_2::@NamedTuple{}, layer_3::@NamedTuple{}}}, Nothing, Bool, Nothing}, 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(1:60, Axis(weight = ViewAxis(1:50, ShapedAxis((10, 5))), bias = ViewAxis(51:60, ShapedAxis((10, 1))))), layer_2 = ViewAxis(61:170, Axis(weight = ViewAxis(1:100, ShapedAxis((10, 10))), bias = ViewAxis(101:110, ShapedAxis((10, 1))))), layer_3 = ViewAxis(171:181, Axis(weight = ViewAxis(1:10, ShapedAxis((1, 10))), bias = ViewAxis(11:11, ShapedAxis((1, 1))))))}}}, SciMLBase.NullParameters, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, @Kwargs{}}, ::Optimisers.Adam; kwargs::@Kwargs{callback::var"#11#18"{var"#loss_function#17"{OptimizationProblem{true, OptimizationFunction{true, AutoZygote, NeuralPDE.var"#full_loss_function#318"{NeuralPDE.var"#null_nonadaptive_loss#118", Vector{NeuralPDE.var"#74#75"{NeuralPDE.var"#219#220"{RuntimeGeneratedFunctions.RuntimeGeneratedFunction{(:cord, Symbol("##θ#228"), :phi, :derivative, :integral, :u, :p), NeuralPDE.var"#_RGF_ModTag", NeuralPDE.var"#_RGF_ModTag", (0x700712a1, 0x6c1c91c2, 0xa5bfc01b, 0x66a91103, 0x0d12fcff), Expr}, NeuralPDE.var"#12#13", NeuralPDE.var"#279#286"{NeuralPDE.var"#279#280#287"{typeof(NeuralPDE.numeric_derivative)}, Dict{Symbol, Int64}, Dict{Symbol, Int64}, GridTraining{Float64}}, typeof(NeuralPDE.numeric_derivative), NeuralPDE.Phi{Chain{@NamedTuple{layer_1::Dense{true, typeof(tanh_fast), typeof(glorot_uniform), typeof(zeros32)}, layer_2::Dense{true, typeof(tanh_fast), typeof(glorot_uniform), typeof(zeros32)}, layer_3::Dense{true, typeof(identity), typeof(glorot_uniform), typeof(zeros32)}}, Nothing}, @NamedTuple{layer_1::@NamedTuple{}, layer_2::@NamedTuple{}, layer_3::@NamedTuple{}}}, Nothing}, Matrix{Real}}}, Vector{NeuralPDE.var"#74#75"{NeuralPDE.var"#219#220"{RuntimeGeneratedFunctions.RuntimeGeneratedFunction{(:cord, Symbol("##θ#228"), :phi, :derivative, :integral, :u, :p), NeuralPDE.var"#_RGF_ModTag", NeuralPDE.var"#_RGF_ModTag", (0xd9696f1d, 0xe356e73c, 0x32906e9c, 0x54a064bc, 0x0cbbe458), Expr}, NeuralPDE.var"#12#13", NeuralPDE.var"#279#286"{NeuralPDE.var"#279#280#287"{typeof(NeuralPDE.numeric_derivative)}, Dict{Symbol, Int64}, Dict{Symbol, Int64}, GridTraining{Float64}}, typeof(NeuralPDE.numeric_derivative), NeuralPDE.Phi{Chain{@NamedTuple{layer_1::Dense{true, typeof(tanh_fast), typeof(glorot_uniform), typeof(zeros32)}, layer_2::Dense{true, typeof(tanh_fast), typeof(glorot_uniform), typeof(zeros32)}, layer_3::Dense{true, typeof(identity), typeof(glorot_uniform), typeof(zeros32)}}, Nothing}, @NamedTuple{layer_1::@NamedTuple{}, layer_2::@NamedTuple{}, layer_3::@NamedTuple{}}}, Nothing}, Matrix{Real}}}, NeuralPDE.PINNRepresentation, Bool, Vector{Int64}, Int64, NeuralPDE.Phi{Chain{@NamedTuple{layer_1::Dense{true, typeof(tanh_fast), typeof(glorot_uniform), typeof(zeros32)}, layer_2::Dense{true, typeof(tanh_fast), typeof(glorot_uniform), typeof(zeros32)}, layer_3::Dense{true, typeof(identity), typeof(glorot_uniform), typeof(zeros32)}}, Nothing}, @NamedTuple{layer_1::@NamedTuple{}, layer_2::@NamedTuple{}, layer_3::@NamedTuple{}}}, Nothing, Bool, Nothing}, 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(1:60, Axis(weight = ViewAxis(1:50, ShapedAxis((10, 5))), bias = ViewAxis(51:60, ShapedAxis((10, 1))))), layer_2 = ViewAxis(61:170, Axis(weight = ViewAxis(1:100, ShapedAxis((10, 10))), bias = ViewAxis(101:110, ShapedAxis((10, 1))))), layer_3 = ViewAxis(171:181, Axis(weight = ViewAxis(1:10, ShapedAxis((1, 10))), bias = ViewAxis(11:11, ShapedAxis((1, 1))))))}}}, SciMLBase.NullParameters, Nothing, Nothing, Nothing, Nothing, Nothing, Nothing, @Kwargs{}}}, Vector{Any}, Vector{Any}, Vector{Any}}, maxiters::Int64}) @ SciMLBase C:\Users\Hp.julia\packages\SciMLBase\szsYq\src\solve.jl:83 [56] allen_cahn(strategy::QuadratureTraining{CubaCuhre, Float64}, minimizer::Optimisers.Adam, maxIters::Int64) @ Main e:\SciMLBenchmarks.jl\benchmarks\PINNErrorsVsTime\jl_notebook_cell_df34fa98e69747e1a8f8a730347b8e2f_W1sZmlsZQ==.jl:105

ChrisRackauckas · 2025-01-19T07:57:34Z

I see, that's for the sampling algorithm. You should only need that on Cuhre?

ParamThakkar123 · 2025-01-19T07:59:24Z

I see, that's for the sampling algorithm. You should only need that on Cuhre?

Yes. But as Cuhre was the first one in the line I thought setting to 1100 just for it would not solve the problem, so I set it to 1100 for all of them

ParamThakkar123 · 2025-01-19T08:00:16Z

The CI has passed here. And all the code seems to run perfectly. Can you please review ??

ChrisRackauckas · 2025-01-19T08:01:28Z

@ArnoStrouwen SciML/Integrals.jl#124 can you remind me what the purpose behind this was?

ArnoStrouwen · 2025-01-19T17:33:11Z

I don't remember myself, but that PR links to:
SciML/Integrals.jl#47

ChrisRackauckas · 2025-01-20T08:50:54Z

Uninitialized memory in the original C: giordano/Cuba.jl#12 (comment) fantastic stuff numerical community, that's your classic method that everyone says when they say "all of the old stuff is robust" 😅

benchmarks/PINNErrorsVsTime/Project.toml

ChrisRackauckas · 2025-01-20T08:53:34Z

Can you force latest majors and make sure the manifest resolves?

ParamThakkar123 · 2025-01-20T10:47:59Z

I bump forced the latest versions and resolved manifests but initially there were a lot of version conflicts. I removed IntegralsCuba and IntegralsCubature for a while to resolve them. The manifest resolved but adding both of them back again poses some more version conflicts

ChrisRackauckas · 2025-01-20T13:58:20Z

Can you share the resolution errors?

ParamThakkar123 · 2025-01-20T16:42:22Z

@ChrisRackauckas These are the resolution errors that occur

ChrisRackauckas · 2025-01-20T18:20:59Z

Oh those were turned into extensions. Change using IntegralsCuba, IntegralsCubature into using Integrals, Cuba, Cubature and change the dependencies to directly depending on Cuba and Cubature.

ParamThakkar123 · 2025-01-20T18:22:09Z

Sure !! 🫡

ParamThakkar123 · 2025-02-08T17:20:11Z

Instead of Stochastic, QuasiRandom

Made the changes

ParamThakkar123 · 2025-02-10T03:38:47Z

@ChrisRackauckas while fixing diffusion_et.jmd I am constantly getting this error

MethodError: no method matching (::MLDataDevices.UnknownDevice)(::Matrix{Float64})

Stacktrace:
  [1] (::NeuralPDE.Phi{StatefulLuxLayer{Static.True, Chain{@NamedTuple{layer_1::Dense{typeof(tanh), Int64, Int64, Nothing, Nothing, Static.True}, layer_2::Dense{typeof(tanh), Int64, Int64, Nothing, Nothing, Static.True}, layer_3::Dense{typeof(identity), Int64, Int64, Nothing, Nothing, Static.True}}, Nothing}, Nothing, @NamedTuple{layer_1::@NamedTuple{}, layer_2::@NamedTuple{}, layer_3::@NamedTuple{}}}})(x::Matrix{Float64}, θ::Optimization.OptimizationState{ComponentArrays.ComponentVector{Float64, Vector{Float64}, Tuple{ComponentArrays.Axis{(layer_1 = ViewAxis(1:30, Axis(weight = ViewAxis(1:20, ShapedAxis((10, 2))), bias = ViewAxis(21:30, Shaped1DAxis((10,))))), layer_2 = ViewAxis(31:140, Axis(weight = ViewAxis(1:100, ShapedAxis((10, 10))), bias = ViewAxis(101:110, Shaped1DAxis((10,))))), layer_3 = ViewAxis(141:151, Axis(weight = ViewAxis(1:10, ShapedAxis((1, 10))), bias = ViewAxis(11:11, Shaped1DAxis((1,))))))}}}, Float64, ComponentArrays.ComponentVector{Float64, Vector{Float64}, Tuple{ComponentArrays.Axis{(layer_1 = ViewAxis(1:30, Axis(weight = ViewAxis(1:20, ShapedAxis((10, 2))), bias = ViewAxis(21:30, Shaped1DAxis((10,))))), layer_2 = ViewAxis(31:140, Axis(weight = ViewAxis(1:100, ShapedAxis((10, 10))), bias = ViewAxis(101:110, Shaped1DAxis((10,))))), layer_3 = ViewAxis(141:151, Axis(weight = ViewAxis(1:10, ShapedAxis((1, 10))), bias = ViewAxis(11:11, Shaped1DAxis((1,))))))}}}, Nothing, Optimisers.Leaf{Optimisers.Adam, Tuple{Vector{Float64}, Vector{Float64}, Tuple{Float64, Float64}}}})
    @ NeuralPDE C:\Users\Hp\.julia\packages\NeuralPDE\nYBAW\src\pinn_types.jl:42
  [2] (::NeuralPDE.var"#7#8")(cord::Matrix{Float64}, θ::Optimization.OptimizationState{ComponentArrays.ComponentVector{Float64, Vector{Float64}, Tuple{ComponentArrays.Axis{(layer_1 = ViewAxis(1:30, Axis(weight = ViewAxis(1:20, ShapedAxis((10, 2))), bias = ViewAxis(21:30, Shaped1DAxis((10,))))), layer_2 = ViewAxis(31:140, Axis(weight = ViewAxis(1:100, ShapedAxis((10, 10))), bias = ViewAxis(101:110, Shaped1DAxis((10,))))), layer_3 = ViewAxis(141:151, Axis(weight = ViewAxis(1:10, ShapedAxis((1, 10))), bias = ViewAxis(11:11, Shaped1DAxis((1,))))))}}}, Float64, ComponentArrays.ComponentVector{Float64, Vector{Float64}, Tuple{ComponentArrays.Axis{(layer_1 = ViewAxis(1:30, Axis(weight = ViewAxis(1:20, ShapedAxis((10, 2))), bias = ViewAxis(21:30, Shaped1DAxis((10,))))), layer_2 = ViewAxis(31:140, Axis(weight = ViewAxis(1:100, ShapedAxis((10, 10))), bias = ViewAxis(101:110, Shaped1DAxis((10,))))), layer_3 = ViewAxis(141:151, Axis(weight = ViewAxis(1:10, ShapedAxis((1, 10))), bias = ViewAxis(11:11, Shaped1DAxis((1,))))))}}}, Nothing, Optimisers.Leaf{Optimisers.Adam, Tuple{Vector{Float64}, Vector{Float64}, Tuple{Float64, Float64}}}}, phi::NeuralPDE.Phi{StatefulLuxLayer{Static.True, Chain{@NamedTuple{layer_1::Dense{typeof(tanh), Int64, Int64, Nothing, Nothing, Static.True}, layer_2::Dense{typeof(tanh), Int64, Int64, Nothing, Nothing, Static.True}, layer_3::Dense{typeof(identity), Int64, Int64, Nothing, Nothing, Static.True}}, Nothing}, Nothing, @NamedTuple{layer_1::@NamedTuple{}, layer_2::@NamedTuple{}, layer_3::@NamedTuple{}}}})
    @ NeuralPDE C:\Users\Hp\.julia\packages\NeuralPDE\nYBAW\src\pinn_types.jl:354
  [3] numeric_derivative(phi::NeuralPDE.Phi{StatefulLuxLayer{Static.True, Chain{@NamedTuple{layer_1::Dense{typeof(tanh), Int64, Int64, Nothing, Nothing, Static.True}, layer_2::Dense{typeof(tanh), Int64, Int64, Nothing, Nothing, Static.True}, layer_3::Dense{typeof(identity), Int64, Int64, Nothing, Nothing, Static.True}}, Nothing}, Nothing, @NamedTuple{layer_1::@NamedTuple{}, layer_2::@NamedTuple{}, layer_3::@NamedTuple{}}}}, u::NeuralPDE.var"#7#8", x::Matrix{Float64}, εs::Vector{Vector{Float64}}, order::Int64, θ::Optimization.OptimizationState{ComponentArrays.ComponentVector{Float64, Vector{Float64}, Tuple{ComponentArrays.Axis{(layer_1 = ViewAxis(1:30, Axis(weight = ViewAxis(1:20, ShapedAxis((10, 2))), bias = ViewAxis(21:30, Shaped1DAxis((10,))))), layer_2 = ViewAxis(31:140, Axis(weight = ViewAxis(1:100, ShapedAxis((10, 10))), bias = ViewAxis(101:110, Shaped1DAxis((10,))))), layer_3 = ViewAxis(141:151, Axis(weight = ViewAxis(1:10, ShapedAxis((1, 10))), bias = ViewAxis(11:11, Shaped1DAxis((1,))))))}}}, Float64, ComponentArrays.ComponentVector{Float64, Vector{Float64}, Tuple{ComponentArrays.Axis{(layer_1 = ViewAxis(1:30, Axis(weight = ViewAxis(1:20, ShapedAxis((10, 2))), bias = ViewAxis(21:30, Shaped1DAxis((10,))))), layer_2 = ViewAxis(31:140, Axis(weight = ViewAxis(1:100, ShapedAxis((10, 10))), bias = ViewAxis(101:110, Shaped1DAxis((10,))))), layer_3 = ViewAxis(141:151, Axis(weight = ViewAxis(1:10, ShapedAxis((1, 10))), bias = ViewAxis(11:11, Shaped1DAxis((1,))))))}}}, Nothing, Optimisers.Leaf{Optimisers.Adam, Tuple{Vector{Float64}, Vector{Float64}, Tuple{Float64, Float64}}}})
    @ NeuralPDE C:\Users\Hp\.julia\packages\NeuralPDE\nYBAW\src\pinn_types.jl:384
  [4] macro expansion
    @ C:\Users\Hp\.julia\packages\NeuralPDE\nYBAW\src\discretize.jl:130 [inlined]
  [5] macro expansion
    @ C:\Users\Hp\.julia\packages\RuntimeGeneratedFunctions\M9ZX8\src\RuntimeGeneratedFunctions.jl:163 [inlined]
  [6] macro expansion
    @ .\none:0 [inlined]
  [7] generated_callfunc(::RuntimeGeneratedFunctions.RuntimeGeneratedFunction{(:cord, Symbol("##θ#226"), :phi, :derivative, :integral, :u, :p), NeuralPDE.var"#_RGF_ModTag", NeuralPDE.var"#_RGF_ModTag", (0x7b9bebea, 0x6a5933cd, 0x4a5f5a8c, 0x91872721, 0xa2d91360), Expr}, ::Matrix{Float64}, ::Optimization.OptimizationState{ComponentArrays.ComponentVector{Float64, Vector{Float64}, Tuple{ComponentArrays.Axis{(layer_1 = ViewAxis(1:30, Axis(weight = ViewAxis(1:20, ShapedAxis((10, 2))), bias = ViewAxis(21:30, Shaped1DAxis((10,))))), layer_2 = ViewAxis(31:140, Axis(weight = ViewAxis(1:100, ShapedAxis((10, 10))), bias = ViewAxis(101:110, Shaped1DAxis((10,))))), layer_3 = ViewAxis(141:151, Axis(weight = ViewAxis(1:10, ShapedAxis((1, 10))), bias = ViewAxis(11:11, Shaped1DAxis((1,))))))}}}, Float64, ComponentArrays.ComponentVector{Float64, Vector{Float64}, Tuple{ComponentArrays.Axis{(layer_1 = ViewAxis(1:30, Axis(weight = ViewAxis(1:20, ShapedAxis((10, 2))), bias = ViewAxis(21:30, Shaped1DAxis((10,))))), layer_2 = ViewAxis(31:140, Axis(weight = ViewAxis(1:100, ShapedAxis((10, 10))), bias = ViewAxis(101:110, Shaped1DAxis((10,))))), layer_3 = ViewAxis(141:151, Axis(weight = ViewAxis(1:10, ShapedAxis((1, 10))), bias = ViewAxis(11:11, Shaped1DAxis((1,))))))}}}, Nothing, Optimisers.Leaf{Optimisers.Adam, Tuple{Vector{Float64}, Vector{Float64}, Tuple{Float64, Float64}}}}, ::NeuralPDE.Phi{StatefulLuxLayer{Static.True, Chain{@NamedTuple{layer_1::Dense{typeof(tanh), Int64, Int64, Nothing, Nothing, Static.True}, layer_2::Dense{typeof(tanh), Int64, Int64, Nothing, Nothing, Static.True}, layer_3::Dense{typeof(identity), Int64, Int64, Nothing, Nothing, Static.True}}, Nothing}, Nothing, @NamedTuple{layer_1::@NamedTuple{}, layer_2::@NamedTuple{}, layer_3::@NamedTuple{}}}}, ::typeof(NeuralPDE.numeric_derivative), ::NeuralPDE.var"#239#246"{NeuralPDE.var"#239#240#247"{typeof(NeuralPDE.numeric_derivative)}, Dict{Symbol, Int64}, Dict{Symbol, Int64}, GridTraining{Vector{Float64}}}, ::NeuralPDE.var"#7#8", ::Nothing)
    @ NeuralPDE .\none:0
  [8] (::RuntimeGeneratedFunctions.RuntimeGeneratedFunction{(:cord, Symbol("##θ#226"), :phi, :derivative, :integral, :u, :p), NeuralPDE.var"#_RGF_ModTag", NeuralPDE.var"#_RGF_ModTag", (0x7b9bebea, 0x6a5933cd, 0x4a5f5a8c, 0x91872721, 0xa2d91360), Expr})(::Matrix{Float64}, ::Optimization.OptimizationState{ComponentArrays.ComponentVector{Float64, Vector{Float64}, Tuple{ComponentArrays.Axis{(layer_1 = ViewAxis(1:30, Axis(weight = ViewAxis(1:20, ShapedAxis((10, 2))), bias = ViewAxis(21:30, Shaped1DAxis((10,))))), layer_2 = ViewAxis(31:140, Axis(weight = ViewAxis(1:100, ShapedAxis((10, 10))), bias = ViewAxis(101:110, Shaped1DAxis((10,))))), layer_3 = ViewAxis(141:151, Axis(weight = ViewAxis(1:10, ShapedAxis((1, 10))), bias = ViewAxis(11:11, Shaped1DAxis((1,))))))}}}, Float64, ComponentArrays.ComponentVector{Float64, Vector{Float64}, Tuple{ComponentArrays.Axis{(layer_1 = ViewAxis(1:30, Axis(weight = ViewAxis(1:20, ShapedAxis((10, 2))), bias = ViewAxis(21:30, Shaped1DAxis((10,))))), layer_2 = ViewAxis(31:140, Axis(weight = ViewAxis(1:100, ShapedAxis((10, 10))), bias = ViewAxis(101:110, Shaped1DAxis((10,))))), layer_3 = ViewAxis(141:151, Axis(weight = ViewAxis(1:10, ShapedAxis((1, 10))), bias = ViewAxis(11:11, Shaped1DAxis((1,))))))}}}, Nothing, Optimisers.Leaf{Optimisers.Adam, Tuple{Vector{Float64}, Vector{Float64}, Tuple{Float64, Float64}}}}, ::NeuralPDE.Phi{StatefulLuxLayer{Static.True, Chain{@NamedTuple{layer_1::Dense{typeof(tanh), Int64, Int64, Nothing, Nothing, Static.True}, layer_2::Dense{typeof(tanh), Int64, Int64, Nothing, Nothing, Static.True}, layer_3::Dense{typeof(identity), Int64, Int64, Nothing, Nothing, Static.True}}, Nothing}, Nothing, @NamedTuple{layer_1::@NamedTuple{}, layer_2::@NamedTuple{}, layer_3::@NamedTuple{}}}}, ::Function, ::Function, ::Function, ::Nothing)
    @ RuntimeGeneratedFunctions C:\Users\Hp\.julia\packages\RuntimeGeneratedFunctions\M9ZX8\src\RuntimeGeneratedFunctions.jl:150
  [9] (::NeuralPDE.var"#197#198"{RuntimeGeneratedFunctions.RuntimeGeneratedFunction{(:cord, Symbol("##θ#226"), :phi, :derivative, :integral, :u, :p), NeuralPDE.var"#_RGF_ModTag", NeuralPDE.var"#_RGF_ModTag", (0x7b9bebea, 0x6a5933cd, 0x4a5f5a8c, 0x91872721, 0xa2d91360), Expr}, NeuralPDE.var"#7#8", NeuralPDE.var"#239#246"{NeuralPDE.var"#239#240#247"{typeof(NeuralPDE.numeric_derivative)}, Dict{Symbol, Int64}, Dict{Symbol, Int64}, GridTraining{Vector{Float64}}}, typeof(NeuralPDE.numeric_derivative), NeuralPDE.Phi{StatefulLuxLayer{Static.True, Chain{@NamedTuple{layer_1::Dense{typeof(tanh), Int64, Int64, Nothing, Nothing, Static.True}, layer_2::Dense{typeof(tanh), Int64, Int64, Nothing, Nothing, Static.True}, layer_3::Dense{typeof(identity), Int64, Int64, Nothing, Nothing, Static.True}}, Nothing}, Nothing, @NamedTuple{layer_1::@NamedTuple{}, layer_2::@NamedTuple{}, layer_3::@NamedTuple{}}}}, Nothing})(cord::Matrix{Float64}, θ::Optimization.OptimizationState{ComponentArrays.ComponentVector{Float64, Vector{Float64}, Tuple{ComponentArrays.Axis{(layer_1 = ViewAxis(1:30, Axis(weight = ViewAxis(1:20, ShapedAxis((10, 2))), bias = ViewAxis(21:30, Shaped1DAxis((10,))))), layer_2 = ViewAxis(31:140, Axis(weight = ViewAxis(1:100, ShapedAxis((10, 10))), bias = ViewAxis(101:110, Shaped1DAxis((10,))))), layer_3 = ViewAxis(141:151, Axis(weight = ViewAxis(1:10, ShapedAxis((1, 10))), bias = ViewAxis(11:11, Shaped1DAxis((1,))))))}}}, Float64, ComponentArrays.ComponentVector{Float64, Vector{Float64}, Tuple{ComponentArrays.Axis{(layer_1 = ViewAxis(1:30, Axis(weight = ViewAxis(1:20, ShapedAxis((10, 2))), bias = ViewAxis(21:30, Shaped1DAxis((10,))))), layer_2 = ViewAxis(31:140, Axis(weight = ViewAxis(1:100, ShapedAxis((10, 10))), bias = ViewAxis(101:110, Shaped1DAxis((10,))))), layer_3 = ViewAxis(141:151, Axis(weight = ViewAxis(1:10, ShapedAxis((1, 10))), bias = ViewAxis(11:11, Shaped1DAxis((1,))))))}}}, Nothing, Optimisers.Leaf{Optimisers.Adam, Tuple{Vector{Float64}, Vector{Float64}, Tuple{Float64, Float64}}}})
    @ NeuralPDE C:\Users\Hp\.julia\packages\NeuralPDE\nYBAW\src\discretize.jl:150
 [10] (::NeuralPDE.var"#78#79"{NeuralPDE.var"#197#198"{RuntimeGeneratedFunctions.RuntimeGeneratedFunction{(:cord, Symbol("##θ#226"), :phi, :derivative, :integral, :u, :p), NeuralPDE.var"#_RGF_ModTag", NeuralPDE.var"#_RGF_ModTag", (0x7b9bebea, 0x6a5933cd, 0x4a5f5a8c, 0x91872721, 0xa2d91360), Expr}, NeuralPDE.var"#7#8", NeuralPDE.var"#239#246"{NeuralPDE.var"#239#240#247"{typeof(NeuralPDE.numeric_derivative)}, Dict{Symbol, Int64}, Dict{Symbol, Int64}, GridTraining{Vector{Float64}}}, typeof(NeuralPDE.numeric_derivative), NeuralPDE.Phi{StatefulLuxLayer{Static.True, Chain{@NamedTuple{layer_1::Dense{typeof(tanh), Int64, Int64, Nothing, Nothing, Static.True}, layer_2::Dense{typeof(tanh), Int64, Int64, Nothing, Nothing, Static.True}, layer_3::Dense{typeof(identity), Int64, Int64, Nothing, Nothing, Static.True}}, Nothing}, Nothing, @NamedTuple{layer_1::@NamedTuple{}, layer_2::@NamedTuple{}, layer_3::@NamedTuple{}}}}, Nothing}, Matrix{Float64}})(θ::Optimization.OptimizationState{ComponentArrays.ComponentVector{Float64, Vector{Float64}, Tuple{ComponentArrays.Axis{(layer_1 = ViewAxis(1:30, Axis(weight = ViewAxis(1:20, ShapedAxis((10, 2))), bias = ViewAxis(21:30, Shaped1DAxis((10,))))), layer_2 = ViewAxis(31:140, Axis(weight = ViewAxis(1:100, ShapedAxis((10, 10))), bias = ViewAxis(101:110, Shaped1DAxis((10,))))), layer_3 = ViewAxis(141:151, Axis(weight = ViewAxis(1:10, ShapedAxis((1, 10))), bias = ViewAxis(11:11, Shaped1DAxis((1,))))))}}}, Float64, ComponentArrays.ComponentVector{Float64, Vector{Float64}, Tuple{ComponentArrays.Axis{(layer_1 = ViewAxis(1:30, Axis(weight = ViewAxis(1:20, ShapedAxis((10, 2))), bias = ViewAxis(21:30, Shaped1DAxis((10,))))), layer_2 = ViewAxis(31:140, Axis(weight = ViewAxis(1:100, ShapedAxis((10, 10))), bias = ViewAxis(101:110, Shaped1DAxis((10,))))), layer_3 = ViewAxis(141:151, Axis(weight = ViewAxis(1:10, ShapedAxis((1, 10))), bias = ViewAxis(11:11, Shaped1DAxis((1,))))))}}}, Nothing, Optimisers.Leaf{Optimisers.Adam, Tuple{Vector{Float64}, Vector{Float64}, Tuple{Float64, Float64}}}})
    @ NeuralPDE C:\Users\Hp\.julia\packages\NeuralPDE\nYBAW\src\training_strategies.jl:70
 [11] (::NeuralPDE.var"#263#284"{Optimization.OptimizationState{ComponentArrays.ComponentVector{Float64, Vector{Float64}, Tuple{ComponentArrays.Axis{(layer_1 = ViewAxis(1:30, Axis(weight = ViewAxis(1:20, ShapedAxis((10, 2))), bias = ViewAxis(21:30, Shaped1DAxis((10,))))), layer_2 = ViewAxis(31:140, Axis(weight = ViewAxis(1:100, ShapedAxis((10, 10))), bias = ViewAxis(101:110, Shaped1DAxis((10,))))), layer_3 = ViewAxis(141:151, Axis(weight = ViewAxis(1:10, ShapedAxis((1, 10))), bias = ViewAxis(11:11, Shaped1DAxis((1,))))))}}}, Float64, ComponentArrays.ComponentVector{Float64, Vector{Float64}, Tuple{ComponentArrays.Axis{(layer_1 = ViewAxis(1:30, Axis(weight = ViewAxis(1:20, ShapedAxis((10, 2))), bias = ViewAxis(21:30, Shaped1DAxis((10,))))), layer_2 = ViewAxis(31:140, Axis(weight = ViewAxis(1:100, ShapedAxis((10, 10))), bias = ViewAxis(101:110, Shaped1DAxis((10,))))), layer_3 = ViewAxis(141:151, Axis(weight = ViewAxis(1:10, ShapedAxis((1, 10))), bias = ViewAxis(11:11, Shaped1DAxis((1,))))))}}}, Nothing, Optimisers.Leaf{Optimisers.Adam, Tuple{Vector{Float64}, Vector{Float64}, Tuple{Float64, Float64}}}}})(pde_loss_function::NeuralPDE.var"#78#79"{NeuralPDE.var"#197#198"{RuntimeGeneratedFunctions.RuntimeGeneratedFunction{(:cord, Symbol("##θ#226"), :phi, :derivative, :integral, :u, :p), NeuralPDE.var"#_RGF_ModTag", NeuralPDE.var"#_RGF_ModTag", (0x7b9bebea, 0x6a5933cd, 0x4a5f5a8c, 0x91872721, 0xa2d91360), Expr}, NeuralPDE.var"#7#8", NeuralPDE.var"#239#246"{NeuralPDE.var"#239#240#247"{typeof(NeuralPDE.numeric_derivative)}, Dict{Symbol, Int64}, Dict{Symbol, Int64}, GridTraining{Vector{Float64}}}, typeof(NeuralPDE.numeric_derivative), NeuralPDE.Phi{StatefulLuxLayer{Static.True, Chain{@NamedTuple{layer_1::Dense{typeof(tanh), Int64, Int64, Nothing, Nothing, Static.True}, layer_2::Dense{typeof(tanh), Int64, Int64, Nothing, Nothing, Static.True}, layer_3::Dense{typeof(identity), Int64, Int64, Nothing, Nothing, Static.True}}, Nothing}, Nothing, @NamedTuple{layer_1::@NamedTuple{}, layer_2::@NamedTuple{}, layer_3::@NamedTuple{}}}}, Nothing}, Matrix{Float64}})
    @ NeuralPDE .\none:0
...
    @ SciMLBase C:\Users\Hp\.julia\packages\SciMLBase\Pma4a\src\solve.jl:95
 [22] diffusion(strategy::GridTraining{Vector{Float64}}, minimizer::Optimisers.Adam, maxIters::Int64)
    @ Main e:\SciMLBenchmarks.jl\benchmarks\PINNErrorsVsTime\jl_notebook_cell_df34fa98e69747e1a8f8a730347b8e2f_W1sZmlsZQ==.jl:68
 [23] top-level scope
    @ e:\SciMLBenchmarks.jl\benchmarks\PINNErrorsVsTime\jl_notebook_cell_df34fa98e69747e1a8f8a730347b8e2f_W3sZmlsZQ==.jl:7

What can be the possible fixes for this ??

ChrisRackauckas · 2025-02-10T16:22:03Z

If I knew I'd do it. It would take time to dig in that I don't have right now.

avik-pal · 2025-02-10T18:44:17Z

  [1] (::NeuralPDE.Phi{StatefulLuxLayer{Static.True, Chain{@NamedTuple{layer_1::Dense{typeof(tanh), Int64, Int64, Nothing, Nothing, Static.True}, layer_2::Dense{typeof(tanh), Int64, Int64, Nothing, Nothing, Static.True}, layer_3::Dense{typeof(identity), Int64, Int64, Nothing, Nothing, Static.True}}, Nothing}, Nothing, @NamedTuple{layer_1::@NamedTuple{}, layer_2::@NamedTuple{}, layer_3::@NamedTuple{}}}})(x::Matrix{Float64}, θ::Optimization.OptimizationState{ComponentArrays.ComponentVector{Float64, Vector{Float64}, Tuple{ComponentArrays.Axis{(layer_1 = ViewAxis(1:30, Axis(weight = ViewAxis(1:20, ShapedAxis((10, 2))), bias = ViewAxis(21:30, Shaped1DAxis((10,))))), layer_2 = ViewAxis(31:140, Axis(weight = ViewAxis(1:100, ShapedAxis((10, 10))), bias = ViewAxis(101:110, Shaped1DAxis((10,))))), layer_3 = ViewAxis(141:151, Axis(weight = ViewAxis(1:10, ShapedAxis((1, 10))), bias = ViewAxis(11:11, Shaped1DAxis((1,))))))}}}, Float64, ComponentArrays.ComponentVector{Float64, Vector{Float64}, Tuple{ComponentArrays.Axis{(layer_1 = ViewAxis(1:30, Axis(weight = ViewAxis(1:20, ShapedAxis((10, 2))), bias = ViewAxis(21:30, Shaped1DAxis((10,))))), layer_2 = ViewAxis(31:140, Axis(weight = ViewAxis(1:100, ShapedAxis((10, 10))), bias = ViewAxis(101:110, Shaped1DAxis((10,))))), layer_3 = ViewAxis(141:151, Axis(weight = ViewAxis(1:10, ShapedAxis((1, 10))), bias = ViewAxis(11:11, Shaped1DAxis((1,))))))}}}, Nothing, Optimisers.Leaf{Optimisers.Adam, Tuple{Vector{Float64}, Vector{Float64}, Tuple{Float64, Float64}}}})
    @ NeuralPDE C:\Users\Hp\.julia\packages\NeuralPDE\nYBAW\src\pinn_types.jl:42
  [2] (::NeuralPDE.var"#7#8")(cord::Matrix{Float64}, θ::Optimization.OptimizationState{ComponentArrays.ComponentVector{Float64, Vector{Float64}, Tuple{ComponentArrays.Axis{(layer_1 = ViewAxis(1:30, Axis(weight = ViewAxis(1:20, ShapedAxis((10, 2))), bias = ViewAxis(21:30, Shaped1DAxis((10,))))), layer_2 = ViewAxis(31:140, Axis(weight = ViewAxis(1:100, ShapedAxis((10, 10))), bias = ViewAxis(101:110, Shaped1DAxis((10,))))), layer_3 = ViewAxis(141:151, Axis(weight = ViewAxis(1:10, ShapedAxis((1, 10))), bias = ViewAxis(11:11, Shaped1DAxis((1,))))))}}}, Float64, ComponentArrays.ComponentVector{Float64, Vector{Float64}, Tuple{ComponentArrays.Axis{(layer_1 = ViewAxis(1:30, Axis(weight = ViewAxis(1:20, ShapedAxis((10, 2))), bias = ViewAxis(21:30, Shaped1DAxis((10,))))), layer_2 = ViewAxis(31:140, Axis(weight = ViewAxis(1:100, ShapedAxis((10, 10))), bias = ViewAxis(101:110, Shaped1DAxis((10,))))), layer_3 = ViewAxis(141:151, Axis(weight = ViewAxis(1:10, ShapedAxis((1, 10))), bias = ViewAxis(11:11, Shaped1DAxis((1,))))))}}}, Nothing, Optimisers.Leaf{Optimisers.Adam, Tuple{Vector{Float64}, Vector{Float64}, Tuple{Float64, Float64}}}}, phi::NeuralPDE.Phi{StatefulLuxLayer{Static.True, Chain{@NamedTuple{layer_1::Dense{typeof(tanh), Int64, Int64, Nothing, Nothing, Static.True}, layer_2::Dense{typeof(tanh), Int64, Int64, Nothing, Nothing, Static.True}, layer_3::Dense{typeof(identity), Int64, Int64, Nothing, Nothing, Static.True}}, Nothing}, Nothing, @NamedTuple{layer_1::@NamedTuple{}, layer_2::@NamedTuple{}, layer_3::@NamedTuple{}}}})
    @ NeuralPDE C:\Users\Hp\.julia\packages\NeuralPDE\nYBAW\src\pinn_types.jl:354

see the Phi type, it is clearly incorrect. It can't contain optimisers leaf which would explain the UnknownDevice type.

You are passing in the optimization state as lux parameters which was deprecated in the last major release. You need to do state.u somewhere in the code

ParamThakkar123 · 2025-02-11T03:10:17Z

  [1] (::NeuralPDE.Phi{StatefulLuxLayer{Static.True, Chain{@NamedTuple{layer_1::Dense{typeof(tanh), Int64, Int64, Nothing, Nothing, Static.True}, layer_2::Dense{typeof(tanh), Int64, Int64, Nothing, Nothing, Static.True}, layer_3::Dense{typeof(identity), Int64, Int64, Nothing, Nothing, Static.True}}, Nothing}, Nothing, @NamedTuple{layer_1::@NamedTuple{}, layer_2::@NamedTuple{}, layer_3::@NamedTuple{}}}})(x::Matrix{Float64}, θ::Optimization.OptimizationState{ComponentArrays.ComponentVector{Float64, Vector{Float64}, Tuple{ComponentArrays.Axis{(layer_1 = ViewAxis(1:30, Axis(weight = ViewAxis(1:20, ShapedAxis((10, 2))), bias = ViewAxis(21:30, Shaped1DAxis((10,))))), layer_2 = ViewAxis(31:140, Axis(weight = ViewAxis(1:100, ShapedAxis((10, 10))), bias = ViewAxis(101:110, Shaped1DAxis((10,))))), layer_3 = ViewAxis(141:151, Axis(weight = ViewAxis(1:10, ShapedAxis((1, 10))), bias = ViewAxis(11:11, Shaped1DAxis((1,))))))}}}, Float64, ComponentArrays.ComponentVector{Float64, Vector{Float64}, Tuple{ComponentArrays.Axis{(layer_1 = ViewAxis(1:30, Axis(weight = ViewAxis(1:20, ShapedAxis((10, 2))), bias = ViewAxis(21:30, Shaped1DAxis((10,))))), layer_2 = ViewAxis(31:140, Axis(weight = ViewAxis(1:100, ShapedAxis((10, 10))), bias = ViewAxis(101:110, Shaped1DAxis((10,))))), layer_3 = ViewAxis(141:151, Axis(weight = ViewAxis(1:10, ShapedAxis((1, 10))), bias = ViewAxis(11:11, Shaped1DAxis((1,))))))}}}, Nothing, Optimisers.Leaf{Optimisers.Adam, Tuple{Vector{Float64}, Vector{Float64}, Tuple{Float64, Float64}}}})
    @ NeuralPDE C:\Users\Hp\.julia\packages\NeuralPDE\nYBAW\src\pinn_types.jl:42
  [2] (::NeuralPDE.var"#7#8")(cord::Matrix{Float64}, θ::Optimization.OptimizationState{ComponentArrays.ComponentVector{Float64, Vector{Float64}, Tuple{ComponentArrays.Axis{(layer_1 = ViewAxis(1:30, Axis(weight = ViewAxis(1:20, ShapedAxis((10, 2))), bias = ViewAxis(21:30, Shaped1DAxis((10,))))), layer_2 = ViewAxis(31:140, Axis(weight = ViewAxis(1:100, ShapedAxis((10, 10))), bias = ViewAxis(101:110, Shaped1DAxis((10,))))), layer_3 = ViewAxis(141:151, Axis(weight = ViewAxis(1:10, ShapedAxis((1, 10))), bias = ViewAxis(11:11, Shaped1DAxis((1,))))))}}}, Float64, ComponentArrays.ComponentVector{Float64, Vector{Float64}, Tuple{ComponentArrays.Axis{(layer_1 = ViewAxis(1:30, Axis(weight = ViewAxis(1:20, ShapedAxis((10, 2))), bias = ViewAxis(21:30, Shaped1DAxis((10,))))), layer_2 = ViewAxis(31:140, Axis(weight = ViewAxis(1:100, ShapedAxis((10, 10))), bias = ViewAxis(101:110, Shaped1DAxis((10,))))), layer_3 = ViewAxis(141:151, Axis(weight = ViewAxis(1:10, ShapedAxis((1, 10))), bias = ViewAxis(11:11, Shaped1DAxis((1,))))))}}}, Nothing, Optimisers.Leaf{Optimisers.Adam, Tuple{Vector{Float64}, Vector{Float64}, Tuple{Float64, Float64}}}}, phi::NeuralPDE.Phi{StatefulLuxLayer{Static.True, Chain{@NamedTuple{layer_1::Dense{typeof(tanh), Int64, Int64, Nothing, Nothing, Static.True}, layer_2::Dense{typeof(tanh), Int64, Int64, Nothing, Nothing, Static.True}, layer_3::Dense{typeof(identity), Int64, Int64, Nothing, Nothing, Static.True}}, Nothing}, Nothing, @NamedTuple{layer_1::@NamedTuple{}, layer_2::@NamedTuple{}, layer_3::@NamedTuple{}}}})
    @ NeuralPDE C:\Users\Hp\.julia\packages\NeuralPDE\nYBAW\src\pinn_types.jl:354

see the Phi type, it is clearly incorrect. It can't contain optimisers leaf which would explain the UnknownDevice type.

You are passing in the optimization state as lux parameters which was deprecated in the last major release. You need to do state.u somewhere in the code

Okay, got your point. This helped. Thanks a lot

ParamThakkar123 · 2025-02-12T02:51:43Z

@ChrisRackauckas I think the HCubatureJL algorithm implementation has some issues because when I replaced particularly this algorithm with GridTraining everything executed perfectly but when I executed the code with HCubatureJL() algorithm, it gave an AssertionError

ParamThakkar123 · 2025-02-16T02:59:52Z

@ChrisRackauckas All the benchmarks in PINNErrorVsTime have been updated and are working fine. Need your review on this.

ChrisRackauckas · 2025-02-16T15:49:20Z

The allen cahn plot looks quite wonky?

ParamThakkar123 · 2025-02-16T15:59:49Z

Might be. I set it at 1100 epochs. Not sure about that but by reading various implementations, documentations, and your suggestions I somehow got this working 😅 . Any suggestions to improvise this implementation ?? I can look in to it

ChrisRackauckas · 2025-02-16T16:03:06Z

I don't see why the x axis should be so large, no lines can be seen.

ParamThakkar123 · 2025-02-16T16:09:43Z

cb = function (p, l)
        try
            deltaT_s = time_ns()
            ctime = time_ns() - startTime - timeCounter
            
            push!(times, ctime / 1e9)
            push!(losses, l)
            push!(error, l)
            
            timeCounter += time_ns() - deltaT_s
            return false
        catch e
            @warn "Callback error: $e"
            return false
        end
    end

Probably due to this line ??

ChrisRackauckas · 2025-02-16T16:13:34Z

Is there one point that is very large in time? Or is the axis being set directly?

ParamThakkar123 · 2025-02-16T16:15:35Z

The axis is being set directly. Haven't predefined anything for time

ParamThakkar123 · 2025-02-16T16:20:22Z

ctime = time_ns() - startTime - timeCounter

Probably it is due to this ??

ChrisRackauckas · 2025-02-16T16:26:31Z

What's the maximum in the array?

ParamThakkar123 · 2025-02-16T16:33:54Z

What's the maximum in the array?

Actually, it's calculating the value of times and adding it to the array. I haven't implemented the code to display it. To find that out I think I need to run the training code again, and it would take around 32 hours to complete training. Any faster method to see the maximum value ??

ParamThakkar123 · 2025-02-16T16:51:46Z

Should I consider running this again @ChrisRackauckas ??

ParamThakkar123 · 2025-02-17T12:24:52Z

@ChrisRackauckas
The maximum value in the times array is
66.796

This value comes from QuasiRandomTraining + BFGS algorithm

ChrisRackauckas · 2025-02-17T12:47:46Z

It is just one time point? It's definitely an outlier. You got rid of the maximum time part: put that back?

ParamThakkar123 · 2025-02-17T12:52:37Z

No. It's an array of two values:
"11" => [66.5992, 66.796]

But I told you the maximum among the two. I haven't removed the maximum time part from the implementation. It's there but the whole code takes around 30 hours to execute for all the algorithms. I just ran this code for the most suspected outlier

ParamThakkar123 · 2025-02-17T20:24:58Z

Any suggestions on further changes on this implementation @ChrisRackauckas ??

ParamThakkar123 · 2025-02-18T03:31:12Z

Training with QuasiRandomTraining:
Dict{Any, Any} with 2 entries:
"12" => [9.60293, 10.3536]
"11" => [79.1825, 79.3383]

Training with QuadratureTraining CubaCuhre():
Dict{Any, Any} with 2 entries:
"12" => [0.0060337, 0.0161921]
"11" => [0.0056472, 0.0096335]

Training with Quadrature Training HCubatureJL()
Dict{Any, Any} with 2 entries:
"12" => [0.0048781, 0.0403565]
"11" => [0.0055059, 0.0097088]

Training Quadrature Training CubatureJLh()
Dict{Any, Any} with 2 entries:
"12" => [0.0099027, 0.0466078]
"11" => [0.0041709, 0.0073577]

Training with Quadrature Training CubaturwJLp()
Dict{Any, Any} with 2 entries:
"12" => [0.0048316, 0.292919]
"11" => [0.0078547, 0.0157797]

Training with GridTraining:
Dict{Any, Any} with 2 entries:
"12" => [0.0088805, 0.120971]
"11" => [0.0086563, 0.0147051]

Training with Stochastic Training:
Dict{Any, Any} with 2 entries:
"12" => [0.0083882, 0.0354763]
"11" => [0.0110269, 0.0161864]

ParamThakkar123 · 2025-02-19T17:18:40Z

@ChrisRackauckas Sorry for bothering you so much despite your busy schedule and this project being on a lower priority but on my end I think this is done because I updated the benchmark codes so that they work as usual. I have studied differential equations, PINNs, etc. but I am yet to explore this one so I am not sure whether this output is satisfactory to you. Your suggestions would be greatly helpful for me to be on a right trajectory so that we reach a satisfactory output.

Also as per the website there are only 2 days left for my claim on this project under the small grants. And this particular piece is the last one mentioned in the list. I am open to contributing to SciML even out of small grants but as of now this is the last one left

ParamThakkar123 added 3 commits January 18, 2025 18:52

PINNErrorVsTime Benchmark Updates

6de0606

Updates

dff54c5

Changed number of iterations

eea28c6

ChrisRackauckas reviewed Jan 20, 2025

View reviewed changes