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NeuralPDE gives a MLDataDevice.UnknownDevice error even though the code is entirely executing on the CPU #922

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ParamThakkar123 opened this issue Feb 3, 2025 · 0 comments
Open

NeuralPDE gives a MLDataDevice.UnknownDevice error even though the code is entirely executing on the CPU #922

ParamThakkar123 opened this issue Feb 3, 2025 · 0 comments
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@ParamThakkar123
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Describe the bug 🐞

NeuralPDE gives a MLDataDevice.UnknownDevice error even though the code is entirely executing on the CPU

Expected behavior

The code should work on the CPU without showing any of these errors

Minimal Reproducible Example 👇

Without MRE, we would only be able to help you to a limited extent, and attention to the issue would be limited. to know more about MRE refer to wikipedia and stackoverflow.

using NeuralPDE
using Integrals, Cubature, Cuba
using ModelingToolkit, Optimization, OptimizationOptimJL
using OptimizationOptimisers
using Lux, Plots
using DelimitedFiles
using QuasiMonteCarlo
import ModelingToolkit: Interval, infimum, supremum

function allen_cahn(strategy, minimizer, maxIters)

    ##  DECLARATIONS
    @parameters t x1 x2 x3 x4
    @variables u(..)

    Dt = Differential(t)
    Dxx1 = Differential(x1)^2
    Dxx2 = Differential(x2)^2
    Dxx3 = Differential(x3)^2
    Dxx4 = Differential(x4)^2

    # Discretization
    tmax = 1.0
    x1width = 1.0
    x2width = 1.0
    x3width = 1.0
    x4width = 1.0

    tMeshNum = 10
    x1MeshNum = 10
    x2MeshNum = 10
    x3MeshNum = 10
    x4MeshNum = 10

    dt = tmax / tMeshNum
    dx1 = x1width / x1MeshNum
    dx2 = x2width / x2MeshNum
    dx3 = x3width / x3MeshNum
    dx4 = x4width / x4MeshNum

    domains = [t ∈ Interval(0.0, tmax),
        x1 ∈ Interval(0.0, x1width),
        x2 ∈ Interval(0.0, x2width),
        x3 ∈ Interval(0.0, x3width),
        x4 ∈ Interval(0.0, x4width)]

    ts = 0.0:dt:tmax
    x1s = 0.0:dx1:x1width
    x2s = 0.0:dx2:x2width
    x3s = 0.0:dx3:x3width
    x4s = 0.0:dx4:x4width

    # Operators
    Δu = Dxx1(u(t, x1, x2, x3, x4)) + Dxx2(u(t, x1, x2, x3, x4)) + Dxx3(u(t, x1, x2, x3, x4)) + Dxx4(u(t, x1, x2, x3, x4)) # Laplacian


    # Equation
    eq = Dt(u(t, x1, x2, x3, x4)) - Δu - u(t, x1, x2, x3, x4) + u(t, x1, x2, x3, x4) * u(t, x1, x2, x3, x4) * u(t, x1, x2, x3, x4) ~ 0  #ALLEN CAHN EQUATION

    initialCondition = 1 / (2 + 0.4 * (x1 * x1 + x2 * x2 + x3 * x3 + x4 * x4)) # see PNAS paper

    bcs = [u(0, x1, x2, x3, x4) ~ initialCondition]  #from literature

    ## NEURAL NETWORK
    n = 10   #neuron number
    chain = Lux.Chain(Lux.Dense(5, n, tanh), Lux.Dense(n, n, tanh), Lux.Dense(n, 1))   #Neural network from OptimizationFlux library

    indvars = [t, x1, x2, x3, x4]   #physically independent variables
    depvars = [u(t, x1, x2, x3, x4)]       #dependent (target) variable

    dim = length(domains)

    losses = []
    error = []
    times = []

    dx_err = 0.2

    error_strategy = GridTraining(dx_err)

    discretization_ = PhysicsInformedNN(chain, error_strategy)
    @named pde_system_ = PDESystem(eq, bcs, domains, indvars, depvars)
    prob_ = discretize(pde_system_, discretization_)

    function loss_function_(θ, p)
        return prob_.f.f(θ, nothing)
    end

    cb_ = function (p, l)
        deltaT_s = time_ns() #Start a clock when the callback begins, this will evaluate questo misurerà anche il calcolo degli uniform error

        ctime = time_ns() - startTime - timeCounter #This variable is the time to use for the time benchmark plot
        append!(times, ctime / 10^9) #Conversion nanosec to seconds
        append!(losses, l)
        loss_ = loss_function_(p, nothing)
        append!(error, loss_)
        timeCounter = timeCounter + time_ns() - deltaT_s #timeCounter sums all delays due to the callback functions of the previous iterations

        #if (ctime/10^9 > time) #if I exceed the limit time I stop the training
        #    return true #Stop the minimizer and continue from line 142
        #end

        return false
    end

    @named pde_system = PDESystem(eq, bcs, domains, indvars, depvars)

    discretization = NeuralPDE.PhysicsInformedNN(chain, strategy)
    prob = NeuralPDE.discretize(pde_system, discretization)

    timeCounter = 0.0
    startTime = time_ns() #Fix initial time (t=0) before starting the training
    res = Optimization.solve(prob, minimizer, callback=cb_, maxiters=maxIters)

    phi = discretization.phi

    params = res.minimizer

    # Model prediction
    domain = [ts, x1s, x2s, x3s, x4s]

    u_predict = [reshape([first(phi([t, x1, x2, x3, x4], res.minimizer)) for x1 in x1s for x2 in x2s for x3 in x3s for x4 in x4s], (length(x1s), length(x2s), length(x3s), length(x4s))) for t in ts]  #matrix of model's prediction

    return [error, params, domain, times, losses]
end

maxIters = [(1,1,1,1,1,1,1000),(1,1,1,1,300,300,300)] #iters for ADAM/LBFGS
# maxIters = [(1,1,1,1,1,1,10),(1,1,1,3,3,3,3)] #iters for ADAM/LBFGS

strategies = [NeuralPDE.QuadratureTraining(quadrature_alg = CubaCuhre(), reltol = 1e-4, abstol = 1e-4, maxiters = 1100),
              NeuralPDE.QuadratureTraining(quadrature_alg = HCubatureJL(), reltol = 1e-4, abstol = 1e-4, maxiters = 1100, batch = 0),
              NeuralPDE.QuadratureTraining(quadrature_alg = CubatureJLh(), reltol = 1e-4, abstol = 1e-4, maxiters = 1100),
              NeuralPDE.QuadratureTraining(quadrature_alg = CubatureJLp(), reltol = 1e-4, abstol = 1e-4, maxiters = 1100),
              NeuralPDE.GridTraining(0.2),
              NeuralPDE.StochasticTraining(400 ; bcs_points= 50),
              NeuralPDE.QuasiRandomTraining(400 ; bcs_points= 50)]

strategies_short_name = ["CubaCuhre",
                        "HCubatureJL",
                        "CubatureJLh",
                        "CubatureJLp",
                        "GridTraining",
                        "StochasticTraining",
                        "QuasiRandomTraining"]

minimizers = [Optimisers.ADAM(0.005),BFGS()]
minimizers_short_name = ["ADAM","BFGS"]

# Run models
error_res =  Dict()
domains = Dict()
params_res = Dict()  #to use same params for the next run
times = Dict()
losses_res = Dict()

for min =1:length(minimizers) # minimizer
    for strat=1:length(strategies) # strategy
          # println(string(strategies_short_name[strat], "  ", minimizers_short_name[min]))
          res = allen_cahn(strategies[strat], minimizers[min], maxIters[min][strat])
          push!(error_res, string(strat,min)     => res[1])
          push!(params_res, string(strat,min) => res[2])
          push!(domains, string(strat,min)        => res[3])
          push!(times, string(strat,min)        => res[4])
          push!(losses_res, string(strat,min)        => res[5])
    end
end

Error & Stacktrace ⚠️

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(1:60, Axis(weight = ViewAxis(1:50, ShapedAxis((10, 5))), bias = ViewAxis(51:60, Shaped1DAxis((10,))))), layer_2 = ViewAxis(61:170, Axis(weight = ViewAxis(1:100, ShapedAxis((10, 10))), bias = ViewAxis(101:110, Shaped1DAxis((10,))))), layer_3 = ViewAxis(171:181, 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(1:60, Axis(weight = ViewAxis(1:50, ShapedAxis((10, 5))), bias = ViewAxis(51:60, Shaped1DAxis((10,))))), layer_2 = ViewAxis(61:170, Axis(weight = ViewAxis(1:100, ShapedAxis((10, 10))), bias = ViewAxis(101:110, Shaped1DAxis((10,))))), layer_3 = ViewAxis(171:181, 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(1:60, Axis(weight = ViewAxis(1:50, ShapedAxis((10, 5))), bias = ViewAxis(51:60, Shaped1DAxis((10,))))), layer_2 = ViewAxis(61:170, Axis(weight = ViewAxis(1:100, ShapedAxis((10, 10))), bias = ViewAxis(101:110, Shaped1DAxis((10,))))), layer_3 = ViewAxis(171:181, 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(1:60, Axis(weight = ViewAxis(1:50, ShapedAxis((10, 5))), bias = ViewAxis(51:60, Shaped1DAxis((10,))))), layer_2 = ViewAxis(61:170, Axis(weight = ViewAxis(1:100, ShapedAxis((10, 10))), bias = ViewAxis(101:110, Shaped1DAxis((10,))))), layer_3 = ViewAxis(171:181, 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(1:60, Axis(weight = ViewAxis(1:50, ShapedAxis((10, 5))), bias = ViewAxis(51:60, Shaped1DAxis((10,))))), layer_2 = ViewAxis(61:170, Axis(weight = ViewAxis(1:100, ShapedAxis((10, 10))), bias = ViewAxis(101:110, Shaped1DAxis((10,))))), layer_3 = ViewAxis(171:181, 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(1:60, Axis(weight = ViewAxis(1:50, ShapedAxis((10, 5))), bias = ViewAxis(51:60, Shaped1DAxis((10,))))), layer_2 = ViewAxis(61:170, Axis(weight = ViewAxis(1:100, ShapedAxis((10, 10))), bias = ViewAxis(101:110, Shaped1DAxis((10,))))), layer_3 = ViewAxis(171:181, 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:382
  [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", (0x875c3d6c, 0x2ff644f8, 0x57c4854c, 0xf710f944, 0x14a70865), Expr}, ::Matrix{Float64}, ::Optimization.OptimizationState{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, Shaped1DAxis((10,))))), layer_2 = ViewAxis(61:170, Axis(weight = ViewAxis(1:100, ShapedAxis((10, 10))), bias = ViewAxis(101:110, Shaped1DAxis((10,))))), layer_3 = ViewAxis(171:181, 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(1:60, Axis(weight = ViewAxis(1:50, ShapedAxis((10, 5))), bias = ViewAxis(51:60, Shaped1DAxis((10,))))), layer_2 = ViewAxis(61:170, Axis(weight = ViewAxis(1:100, ShapedAxis((10, 10))), bias = ViewAxis(101:110, Shaped1DAxis((10,))))), layer_3 = ViewAxis(171:181, 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{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", (0x875c3d6c, 0x2ff644f8, 0x57c4854c, 0xf710f944, 0x14a70865), Expr})(::Matrix{Float64}, ::Optimization.OptimizationState{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, Shaped1DAxis((10,))))), layer_2 = ViewAxis(61:170, Axis(weight = ViewAxis(1:100, ShapedAxis((10, 10))), bias = ViewAxis(101:110, Shaped1DAxis((10,))))), layer_3 = ViewAxis(171:181, 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(1:60, Axis(weight = ViewAxis(1:50, ShapedAxis((10, 5))), bias = ViewAxis(51:60, Shaped1DAxis((10,))))), layer_2 = ViewAxis(61:170, Axis(weight = ViewAxis(1:100, ShapedAxis((10, 10))), bias = ViewAxis(101:110, Shaped1DAxis((10,))))), layer_3 = ViewAxis(171:181, 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", (0x875c3d6c, 0x2ff644f8, 0x57c4854c, 0xf710f944, 0x14a70865), 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{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(1:60, Axis(weight = ViewAxis(1:50, ShapedAxis((10, 5))), bias = ViewAxis(51:60, Shaped1DAxis((10,))))), layer_2 = ViewAxis(61:170, Axis(weight = ViewAxis(1:100, ShapedAxis((10, 10))), bias = ViewAxis(101:110, Shaped1DAxis((10,))))), layer_3 = ViewAxis(171:181, 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(1:60, Axis(weight = ViewAxis(1:50, ShapedAxis((10, 5))), bias = ViewAxis(51:60, Shaped1DAxis((10,))))), layer_2 = ViewAxis(61:170, Axis(weight = ViewAxis(1:100, ShapedAxis((10, 10))), bias = ViewAxis(101:110, Shaped1DAxis((10,))))), layer_3 = ViewAxis(171:181, 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", (0x875c3d6c, 0x2ff644f8, 0x57c4854c, 0xf710f944, 0x14a70865), 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{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(1:60, Axis(weight = ViewAxis(1:50, ShapedAxis((10, 5))), bias = ViewAxis(51:60, Shaped1DAxis((10,))))), layer_2 = ViewAxis(61:170, Axis(weight = ViewAxis(1:100, ShapedAxis((10, 10))), bias = ViewAxis(101:110, Shaped1DAxis((10,))))), layer_3 = ViewAxis(171:181, 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(1:60, Axis(weight = ViewAxis(1:50, ShapedAxis((10, 5))), bias = ViewAxis(51:60, Shaped1DAxis((10,))))), layer_2 = ViewAxis(61:170, Axis(weight = ViewAxis(1:100, ShapedAxis((10, 10))), bias = ViewAxis(101:110, Shaped1DAxis((10,))))), layer_3 = ViewAxis(171:181, 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(1:60, Axis(weight = ViewAxis(1:50, ShapedAxis((10, 5))), bias = ViewAxis(51:60, Shaped1DAxis((10,))))), layer_2 = ViewAxis(61:170, Axis(weight = ViewAxis(1:100, ShapedAxis((10, 10))), bias = ViewAxis(101:110, Shaped1DAxis((10,))))), layer_3 = ViewAxis(171:181, 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(1:60, Axis(weight = ViewAxis(1:50, ShapedAxis((10, 5))), bias = ViewAxis(51:60, Shaped1DAxis((10,))))), layer_2 = ViewAxis(61:170, Axis(weight = ViewAxis(1:100, ShapedAxis((10, 10))), bias = ViewAxis(101:110, Shaped1DAxis((10,))))), layer_3 = ViewAxis(171:181, 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", (0x875c3d6c, 0x2ff644f8, 0x57c4854c, 0xf710f944, 0x14a70865), 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{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] allen_cahn(strategy::QuadratureTraining{Float64, CubaCuhre}, minimizer::Optimisers.Adam, maxIters::Int64)
    @ Main e:\SciMLBenchmarks.jl\benchmarks\PINNErrorsVsTime\jl_notebook_cell_df34fa98e69747e1a8f8a730347b8e2f_W0sZmlsZQ==.jl:114
 [23] top-level scope
    @ e:\SciMLBenchmarks.jl\benchmarks\PINNErrorsVsTime\jl_notebook_cell_df34fa98e69747e1a8f8a730347b8e2f_W2sZmlsZQ==.jl:4

Environment (please complete the following information):

  • Output of using Pkg; Pkg.status()
Status `E:\SciMLBenchmarks.jl\benchmarks\PINNErrorsVsTime\Project.toml`
  [8a292aeb] Cuba v2.3.0
  [667455a9] Cubature v1.5.1
  [8bb1440f] DelimitedFiles v1.9.1
  [de52edbc] Integrals v4.5.0
⌃ [b2108857] Lux v1.2.3
⌃ [7e8f7934] MLDataDevices v1.5.3
  [961ee093] ModelingToolkit v9.62.0
  [315f7962] NeuralPDE v5.17.0
  [7f7a1694] Optimization v4.1.0
  [36348300] OptimizationOptimJL v0.4.1
  [42dfb2eb] OptimizationOptimisers v0.3.7
  [91a5bcdd] Plots v1.40.9
  [8a4e6c94] QuasiMonteCarlo v0.3.3
  [31c91b34] SciMLBenchmarks v0.1.3
Info Packages marked with ⌃ have new versions available and may be upgradable.
  • Output of using Pkg; Pkg.status(; mode = PKGMODE_MANIFEST)
Status `E:\SciMLBenchmarks.jl\benchmarks\PINNErrorsVsTime\Manifest.toml`
  [47edcb42] ADTypes v1.12.1
  [621f4979] AbstractFFTs v1.5.0
  [80f14c24] AbstractMCMC v5.6.0
  [1520ce14] AbstractTrees v0.4.5
  [7d9f7c33] Accessors v0.1.41
  [79e6a3ab] Adapt v4.1.1
  [0bf59076] AdvancedHMC v0.6.4
  [66dad0bd] AliasTables v1.1.3
  [dce04be8] ArgCheck v2.4.0
  [ec485272] ArnoldiMethod v0.4.0
  [4fba245c] ArrayInterface v7.18.0
  [4c555306] ArrayLayouts v1.11.0
  [a9b6321e] Atomix v1.1.0
  [13072b0f] AxisAlgorithms v1.1.0
  [39de3d68] AxisArrays v0.4.7
  [198e06fe] BangBang v0.4.3
  [9718e550] Baselet v0.1.1
  [e2ed5e7c] Bijections v0.1.9
  [d1d4a3ce] BitFlags v0.1.9
  [62783981] BitTwiddlingConvenienceFunctions v0.1.6
  [8e7c35d0] BlockArrays v1.3.0
  [70df07ce] BracketingNonlinearSolve v1.1.0
  [fa961155] CEnum v0.5.0
  [2a0fbf3d] CPUSummary v0.2.6
  [00ebfdb7] CSTParser v3.4.3
  [082447d4] ChainRules v1.72.2
  [d360d2e6] ChainRulesCore v1.25.1
  [fb6a15b2] CloseOpenIntervals v0.1.13
  [944b1d66] CodecZlib v0.7.7
  [35d6a980] ColorSchemes v3.28.0
  [3da002f7] ColorTypes v0.12.0
  [c3611d14] ColorVectorSpace v0.11.0
  [5ae59095] Colors v0.13.0
  [861a8166] Combinatorics v1.0.2
  [a80b9123] CommonMark v0.8.15
  [38540f10] CommonSolve v0.2.4
  [bbf7d656] CommonSubexpressions v0.3.1
  [f70d9fcc] CommonWorldInvalidations v1.0.0
  [34da2185] Compat v4.16.0
  [b0b7db55] ComponentArrays v0.15.23
  [b152e2b5] CompositeTypes v0.1.4
  [a33af91c] CompositionsBase v0.1.2
  [2569d6c7] ConcreteStructs v0.2.3
  [f0e56b4a] ConcurrentUtilities v2.4.3
  [8f4d0f93] Conda v1.10.2
  [88cd18e8] ConsoleProgressMonitor v0.1.2
  [187b0558] ConstructionBase v1.5.8
  [d38c429a] Contour v0.6.3
  [adafc99b] CpuId v0.3.1
  [a8cc5b0e] Crayons v4.1.1
  [8a292aeb] Cuba v2.3.0
  [667455a9] Cubature v1.5.1
  [9a962f9c] DataAPI v1.16.0
  [864edb3b] DataStructures v0.18.20
  [e2d170a0] DataValueInterfaces v1.0.0
  [244e2a9f] DefineSingletons v0.1.2
  [8bb1440f] DelimitedFiles v1.9.1
  [2b5f629d] DiffEqBase v6.161.0
⌃ [459566f4] DiffEqCallbacks v4.1.0
  [77a26b50] DiffEqNoiseProcess v5.24.1
  [163ba53b] DiffResults v1.1.0
  [b552c78f] DiffRules v1.15.1
  [a0c0ee7d] DifferentiationInterface v0.6.39
  [8d63f2c5] DispatchDoctor v0.4.19
  [31c24e10] Distributions v0.25.117
  [ffbed154] DocStringExtensions v0.9.3
  [5b8099bc] DomainSets v0.7.15
  [7c1d4256] DynamicPolynomials v0.6.1
  [06fc5a27] DynamicQuantities v1.4.0
  [4e289a0a] EnumX v1.0.4
  [f151be2c] EnzymeCore v0.8.8
  [460bff9d] ExceptionUnwrapping v0.1.11
  [e2ba6199] ExprTools v0.1.10
⌅ [6b7a57c9] Expronicon v0.8.5
  [c87230d0] FFMPEG v0.4.2
  [7a1cc6ca] FFTW v1.8.1
  [7034ab61] FastBroadcast v0.3.5
  [9aa1b823] FastClosures v0.3.2
  [29a986be] FastLapackInterface v2.0.4
  [a4df4552] FastPower v1.1.1
  [1a297f60] FillArrays v1.13.0
  [64ca27bc] FindFirstFunctions v1.4.1
  [6a86dc24] FiniteDiff v2.27.0
  [53c48c17] FixedPointNumbers v0.8.5
  [1fa38f19] Format v1.3.7
  [f6369f11] ForwardDiff v0.10.38
  [069b7b12] FunctionWrappers v1.1.3
  [77dc65aa] FunctionWrappersWrappers v0.1.3
⌅ [d9f16b24] Functors v0.4.12
  [0c68f7d7] GPUArrays v11.2.1
  [46192b85] GPUArraysCore v0.2.0
  [28b8d3ca] GR v0.73.12
  [c145ed77] GenericSchur v0.5.4
  [d7ba0133] Git v1.3.1
  [c27321d9] Glob v1.3.1
  [86223c79] Graphs v1.12.0
  [42e2da0e] Grisu v1.0.2
  [19dc6840] HCubature v1.7.0
  [cd3eb016] HTTP v1.10.15
  [076d061b] HashArrayMappedTries v0.2.0
  [eafb193a] Highlights v0.5.3
  [3e5b6fbb] HostCPUFeatures v0.1.17
  [0e44f5e4] Hwloc v3.3.0
  [34004b35] HypergeometricFunctions v0.3.27
  [7073ff75] IJulia v1.26.0
  [7869d1d1] IRTools v0.4.14
  [615f187c] IfElse v0.1.1
  [d25df0c9] Inflate v0.1.5
  [22cec73e] InitialValues v0.3.1
  [505f98c9] InplaceOps v0.3.0
  [18e54dd8] IntegerMathUtils v0.1.2
  [de52edbc] Integrals v4.5.0
  [a98d9a8b] Interpolations v0.15.1
  [8197267c] IntervalSets v0.7.10
  [3587e190] InverseFunctions v0.1.17
  [92d709cd] IrrationalConstants v0.2.4
  [c8e1da08] IterTools v1.10.0
  [82899510] IteratorInterfaceExtensions v1.0.0
  [1019f520] JLFzf v0.1.9
  [692b3bcd] JLLWrappers v1.7.0
  [682c06a0] JSON v0.21.4
  [98e50ef6] JuliaFormatter v1.0.62
  [ccbc3e58] JumpProcesses v9.14.1
  [ef3ab10e] KLU v0.6.0
  [63c18a36] KernelAbstractions v0.9.33
  [5ab0869b] KernelDensity v0.6.9
  [ba0b0d4f] Krylov v0.9.9
  [5be7bae1] LBFGSB v0.4.1
  [929cbde3] LLVM v9.2.0
  [b964fa9f] LaTeXStrings v1.4.0
  [23fbe1c1] Latexify v0.16.5
  [73f95e8e] LatticeRules v0.0.1
  [10f19ff3] LayoutPointers v0.1.17
  [5078a376] LazyArrays v2.4.0
  [1d6d02ad] LeftChildRightSiblingTrees v0.2.0
  [87fe0de2] LineSearch v0.1.4
  [d3d80556] LineSearches v7.3.0
  [7ed4a6bd] LinearSolve v2.38.0
  [6fdf6af0] LogDensityProblems v2.1.2
  [996a588d] LogDensityProblemsAD v1.13.0
  [2ab3a3ac] LogExpFunctions v0.3.29
  [e6f89c97] LoggingExtras v1.1.0
  [bdcacae8] LoopVectorization v0.12.171
⌃ [b2108857] Lux v1.2.3
⌃ [bb33d45b] LuxCore v1.1.0
⌃ [82251201] LuxLib v1.3.7
  [c7f686f2] MCMCChains v6.0.7
  [be115224] MCMCDiagnosticTools v0.3.14
⌃ [7e8f7934] MLDataDevices v1.5.3
  [e80e1ace] MLJModelInterface v1.11.0
  [d8e11817] MLStyle v0.4.17
  [1914dd2f] MacroTools v0.5.15
  [d125e4d3] ManualMemory v0.1.8
  [bb5d69b7] MaybeInplace v0.1.4
  [739be429] MbedTLS v1.1.9
  [442fdcdd] Measures v0.3.2
  [128add7d] MicroCollections v0.2.0
  [e1d29d7a] Missings v1.2.0
  [961ee093] ModelingToolkit v9.62.0
  [4886b29c] MonteCarloIntegration v0.2.0
  [0987c9cc] MonteCarloMeasurements v1.4.3
  [46d2c3a1] MuladdMacro v0.2.4
  [102ac46a] MultivariatePolynomials v0.5.7
  [ffc61752] Mustache v1.0.20
  [d8a4904e] MutableArithmetics v1.6.3
  [d41bc354] NLSolversBase v7.8.3
  [872c559c] NNlib v0.9.27
  [77ba4419] NaNMath v1.1.2
  [c020b1a1] NaturalSort v1.0.0
  [315f7962] NeuralPDE v5.17.0
  [8913a72c] NonlinearSolve v4.3.0
  [be0214bd] NonlinearSolveBase v1.4.0
  [5959db7a] NonlinearSolveFirstOrder v1.2.0
  [9a2c21bd] NonlinearSolveQuasiNewton v1.1.0
  [26075421] NonlinearSolveSpectralMethods v1.1.0
  [6fe1bfb0] OffsetArrays v1.15.0
  [4d8831e6] OpenSSL v1.4.3
  [429524aa] Optim v1.11.0
⌅ [3bd65402] Optimisers v0.3.4
  [7f7a1694] Optimization v4.1.0
  [bca83a33] OptimizationBase v2.4.0
  [36348300] OptimizationOptimJL v0.4.1
  [42dfb2eb] OptimizationOptimisers v0.3.7
  [bac558e1] OrderedCollections v1.8.0
  [90014a1f] PDMats v0.11.32
  [d96e819e] Parameters v0.12.3
  [69de0a69] Parsers v2.8.1
  [b98c9c47] Pipe v1.3.0
  [ccf2f8ad] PlotThemes v3.3.0
  [995b91a9] PlotUtils v1.4.3
  [91a5bcdd] Plots v1.40.9
  [e409e4f3] PoissonRandom v0.4.4
  [f517fe37] Polyester v0.7.16
  [1d0040c9] PolyesterWeave v0.2.2
  [85a6dd25] PositiveFactorizations v0.2.4
  [d236fae5] PreallocationTools v0.4.24
  [aea7be01] PrecompileTools v1.2.1
  [21216c6a] Preferences v1.4.3
  [08abe8d2] PrettyTables v2.4.0
  [27ebfcd6] Primes v0.5.6
  [33c8b6b6] ProgressLogging v0.1.4
  [92933f4c] ProgressMeter v1.10.2
  [43287f4e] PtrArrays v1.3.0
  [1fd47b50] QuadGK v2.11.1
  [8a4e6c94] QuasiMonteCarlo v0.3.3
  [74087812] Random123 v1.7.0
  [e6cf234a] RandomNumbers v1.6.0
  [b3c3ace0] RangeArrays v0.3.2
  [c84ed2f1] Ratios v0.4.5
  [c1ae055f] RealDot v0.1.0
  [3cdcf5f2] RecipesBase v1.3.4
  [01d81517] RecipesPipeline v0.6.12
  [731186ca] RecursiveArrayTools v3.28.0
  [f2c3362d] RecursiveFactorization v0.2.23
  [189a3867] Reexport v1.2.2
  [05181044] RelocatableFolders v1.0.1
  [ae029012] Requires v1.3.0
  [ae5879a3] ResettableStacks v1.1.1
  [79098fc4] Rmath v0.8.0
  [7e49a35a] RuntimeGeneratedFunctions v0.5.13
  [9dfe8606] SCCNonlinearSolve v1.0.0
  [94e857df] SIMDTypes v0.1.0
  [476501e8] SLEEFPirates v0.6.43
  [0bca4576] SciMLBase v2.72.2
  [31c91b34] SciMLBenchmarks v0.1.3
  [19f34311] SciMLJacobianOperators v0.1.1
  [c0aeaf25] SciMLOperators v0.3.12
  [53ae85a6] SciMLStructures v1.6.1
  [30f210dd] ScientificTypesBase v3.0.0
  [7e506255] ScopedValues v1.3.0
  [6c6a2e73] Scratch v1.2.1
  [efcf1570] Setfield v1.1.1
  [992d4aef] Showoff v1.0.3
  [777ac1f9] SimpleBufferStream v1.2.0
  [727e6d20] SimpleNonlinearSolve v2.1.0
  [699a6c99] SimpleTraits v0.9.4
  [ce78b400] SimpleUnPack v1.1.0
  [ed01d8cd] Sobol v1.5.0
  [b85f4697] SoftGlobalScope v1.1.0
  [a2af1166] SortingAlgorithms v1.2.1
  [9f842d2f] SparseConnectivityTracer v0.6.10
  [dc90abb0] SparseInverseSubset v0.1.2
  [0a514795] SparseMatrixColorings v0.4.12
  [e56a9233] Sparspak v0.3.9
  [276daf66] SpecialFunctions v2.5.0
  [171d559e] SplittablesBase v0.1.15
  [860ef19b] StableRNGs v1.0.2
  [aedffcd0] Static v1.1.1
  [0d7ed370] StaticArrayInterface v1.8.0
  [90137ffa] StaticArrays v1.9.11
  [1e83bf80] StaticArraysCore v1.4.3
  [64bff920] StatisticalTraits v3.4.0
  [82ae8749] StatsAPI v1.7.0
  [2913bbd2] StatsBase v0.34.4
  [4c63d2b9] StatsFuns v1.3.2
  [7792a7ef] StrideArraysCore v0.5.7
  [69024149] StringEncodings v0.3.7
  [892a3eda] StringManipulation v0.4.0
⌃ [09ab397b] StructArrays v0.6.21
  [2efcf032] SymbolicIndexingInterface v0.3.37
  [19f23fe9] SymbolicLimits v0.2.2
  [d1185830] SymbolicUtils v3.13.0
  [0c5d862f] Symbolics v6.27.0
  [3783bdb8] TableTraits v1.0.1
  [bd369af6] Tables v1.12.0
  [62fd8b95] TensorCore v0.1.1
  [8ea1fca8] TermInterface v2.0.0
  [5d786b92] TerminalLoggers v0.1.7
  [1c621080] TestItems v1.0.0
  [8290d209] ThreadingUtilities v0.5.2
  [a759f4b9] TimerOutputs v0.5.26
  [0796e94c] Tokenize v0.5.29
  [3bb67fe8] TranscodingStreams v0.11.3
  [28d57a85] Transducers v0.4.84
  [d5829a12] TriangularSolve v0.2.1
  [410a4b4d] Tricks v0.1.10
  [781d530d] TruncatedStacktraces v1.4.0
  [5c2747f8] URIs v1.5.1
  [3a884ed6] UnPack v1.0.2
  [1cfade01] UnicodeFun v0.4.1
  [1986cc42] Unitful v1.22.0
  [45397f5d] UnitfulLatexify v1.6.4
  [a7c27f48] Unityper v0.1.6
  [013be700] UnsafeAtomics v0.3.0
  [41fe7b60] Unzip v0.2.0
  [3d5dd08c] VectorizationBase v0.21.71
  [81def892] VersionParsing v1.3.0
  [897b6980] WeakValueDicts v0.1.0
  [44d3d7a6] Weave v0.10.12
  [d49dbf32] WeightInitializers v1.1.1
  [efce3f68] WoodburyMatrices v1.0.0
  [ddb6d928] YAML v0.4.12
  [c2297ded] ZMQ v1.4.0
⌅ [e88e6eb3] Zygote v0.6.75
  [700de1a5] ZygoteRules v0.2.7
  [6e34b625] Bzip2_jll v1.0.9+0
  [83423d85] Cairo_jll v1.18.2+1
  [3bed1096] Cuba_jll v4.2.2+1
  [7bc98958] Cubature_jll v1.0.5+0
  [ee1fde0b] Dbus_jll v1.14.10+0
  [2702e6a9] EpollShim_jll v0.0.20230411+1
  [2e619515] Expat_jll v2.6.5+0
⌅ [b22a6f82] FFMPEG_jll v4.4.4+1
  [f5851436] FFTW_jll v3.3.10+3
  [a3f928ae] Fontconfig_jll v2.15.0+0
  [d7e528f0] FreeType2_jll v2.13.3+1
  [559328eb] FriBidi_jll v1.0.16+0
  [0656b61e] GLFW_jll v3.4.0+2
  [d2c73de3] GR_jll v0.73.12+0
  [78b55507] Gettext_jll v0.21.0+0
  [f8c6e375] Git_jll v2.47.1+0
  [7746bdde] Glib_jll v2.82.4+0
  [3b182d85] Graphite2_jll v1.3.14+1
  [2e76f6c2] HarfBuzz_jll v8.5.0+0
  [e33a78d0] Hwloc_jll v2.11.2+3
  [1d5cc7b8] IntelOpenMP_jll v2025.0.4+0
  [aacddb02] JpegTurbo_jll v3.1.1+0
  [c1c5ebd0] LAME_jll v3.100.2+0
  [88015f11] LERC_jll v4.0.1+0
  [dad2f222] LLVMExtra_jll v0.0.35+0
  [1d63c593] LLVMOpenMP_jll v18.1.7+0
  [dd4b983a] LZO_jll v2.10.3+0
  [81d17ec3] L_BFGS_B_jll v3.0.1+0
⌅ [e9f186c6] Libffi_jll v3.2.2+2
  [d4300ac3] Libgcrypt_jll v1.11.0+0
  [7e76a0d4] Libglvnd_jll v1.7.0+0
  [7add5ba3] Libgpg_error_jll v1.51.1+0
  [94ce4f54] Libiconv_jll v1.18.0+0
  [4b2f31a3] Libmount_jll v2.40.3+0
  [89763e89] Libtiff_jll v4.7.1+0
  [38a345b3] Libuuid_jll v2.40.3+0
  [856f044c] MKL_jll v2025.0.1+1
  [e7412a2a] Ogg_jll v1.3.5+1
  [458c3c95] OpenSSL_jll v3.0.15+3
  [efe28fd5] OpenSpecFun_jll v0.5.6+0
  [91d4177d] Opus_jll v1.3.3+0
  [36c8627f] Pango_jll v1.55.5+0
⌅ [30392449] Pixman_jll v0.43.4+0
⌅ [c0090381] Qt6Base_jll v6.7.1+1
  [629bc702] Qt6Declarative_jll v6.7.1+2
  [ce943373] Qt6ShaderTools_jll v6.7.1+1
  [e99dba38] Qt6Wayland_jll v6.7.1+1
  [f50d1b31] Rmath_jll v0.5.1+0
  [a44049a8] Vulkan_Loader_jll v1.3.243+0
  [a2964d1f] Wayland_jll v1.21.0+2
  [2381bf8a] Wayland_protocols_jll v1.36.0+0
  [02c8fc9c] XML2_jll v2.13.5+0
  [aed1982a] XSLT_jll v1.1.42+0
  [ffd25f8a] XZ_jll v5.6.4+1
  [f67eecfb] Xorg_libICE_jll v1.1.1+0
  [c834827a] Xorg_libSM_jll v1.2.4+0
  [4f6342f7] Xorg_libX11_jll v1.8.6+3
  [0c0b7dd1] Xorg_libXau_jll v1.0.12+0
  [935fb764] Xorg_libXcursor_jll v1.2.3+0
  [a3789734] Xorg_libXdmcp_jll v1.1.5+0
  [1082639a] Xorg_libXext_jll v1.3.6+3
  [d091e8ba] Xorg_libXfixes_jll v6.0.0+0
  [a51aa0fd] Xorg_libXi_jll v1.8.2+0
  [d1454406] Xorg_libXinerama_jll v1.1.5+0
  [ec84b674] Xorg_libXrandr_jll v1.5.4+0
  [ea2f1a96] Xorg_libXrender_jll v0.9.11+1
  [14d82f49] Xorg_libpthread_stubs_jll v0.1.2+0
  [c7cfdc94] Xorg_libxcb_jll v1.17.0+3
  [cc61e674] Xorg_libxkbfile_jll v1.1.2+1
  [e920d4aa] Xorg_xcb_util_cursor_jll v0.1.4+0
  [12413925] Xorg_xcb_util_image_jll v0.4.0+1
  [2def613f] Xorg_xcb_util_jll v0.4.0+1
  [975044d2] Xorg_xcb_util_keysyms_jll v0.4.0+1
  [0d47668e] Xorg_xcb_util_renderutil_jll v0.3.9+1
  [c22f9ab0] Xorg_xcb_util_wm_jll v0.4.1+1
  [35661453] Xorg_xkbcomp_jll v1.4.6+1
  [33bec58e] Xorg_xkeyboard_config_jll v2.39.0+0
  [c5fb5394] Xorg_xtrans_jll v1.5.1+0
  [8f1865be] ZeroMQ_jll v4.3.5+3
  [3161d3a3] Zstd_jll v1.5.7+0
  [35ca27e7] eudev_jll v3.2.9+0
  [214eeab7] fzf_jll v0.56.3+0
  [1a1c6b14] gperf_jll v3.1.1+1
  [a4ae2306] libaom_jll v3.11.0+0
  [0ac62f75] libass_jll v0.15.2+0
  [1183f4f0] libdecor_jll v0.2.2+0
  [2db6ffa8] libevdev_jll v1.11.0+0
  [f638f0a6] libfdk_aac_jll v2.0.3+0
  [36db933b] libinput_jll v1.18.0+0
  [b53b4c65] libpng_jll v1.6.46+0
  [a9144af2] libsodium_jll v1.0.20+3
  [f27f6e37] libvorbis_jll v1.3.7+2
  [009596ad] mtdev_jll v1.1.6+0
  [1317d2d5] oneTBB_jll v2021.12.0+0
⌅ [1270edf5] x264_jll v2021.5.5+0
⌅ [dfaa095f] x265_jll v3.5.0+0
  [d8fb68d0] xkbcommon_jll v1.4.1+2
  [0dad84c5] ArgTools v1.1.1
  [56f22d72] Artifacts
  [2a0f44e3] Base64
  [ade2ca70] Dates
  [8ba89e20] Distributed
  [f43a241f] Downloads v1.6.0
  [7b1f6079] FileWatching
  [9fa8497b] Future
  [b77e0a4c] InteractiveUtils
  [4af54fe1] LazyArtifacts
  [b27032c2] LibCURL v0.6.4
  [76f85450] LibGit2
  [8f399da3] Libdl
  [37e2e46d] LinearAlgebra
  [56ddb016] Logging
  [d6f4376e] Markdown
  [a63ad114] Mmap
  [ca575930] NetworkOptions v1.2.0
  [44cfe95a] Pkg v1.10.0
  [de0858da] Printf
  [3fa0cd96] REPL
  [9a3f8284] Random
  [ea8e919c] SHA v0.7.0
  [9e88b42a] Serialization
  [1a1011a3] SharedArrays
  [6462fe0b] Sockets
  [2f01184e] SparseArrays v1.10.0
  [10745b16] Statistics v1.10.0
  [4607b0f0] SuiteSparse
  [fa267f1f] TOML v1.0.3
  [a4e569a6] Tar v1.10.0
  [8dfed614] Test
  [cf7118a7] UUIDs
  [4ec0a83e] Unicode
  [e66e0078] CompilerSupportLibraries_jll v1.1.1+0
  [deac9b47] LibCURL_jll v8.4.0+0
  [e37daf67] LibGit2_jll v1.6.4+0
  [29816b5a] LibSSH2_jll v1.11.0+1
  [c8ffd9c3] MbedTLS_jll v2.28.2+1
  [14a3606d] MozillaCACerts_jll v2023.1.10
  [4536629a] OpenBLAS_jll v0.3.23+4
  [05823500] OpenLibm_jll v0.8.1+2
  [efcefdf7] PCRE2_jll v10.42.0+1
  [bea87d4a] SuiteSparse_jll v7.2.1+1
  [83775a58] Zlib_jll v1.2.13+1
  [8e850b90] libblastrampoline_jll v5.11.0+0
  [8e850ede] nghttp2_jll v1.52.0+1
  [3f19e933] p7zip_jll v17.4.0+2
Info Packages marked with ⌃ and ⌅ have new versions available. Those with ⌃ may be upgradable, but those with ⌅ are restricted by compatibility constraints from upgrading. To see why use `status --outdated -m`
  • Output of versioninfo()
Julia Version 1.10.7
Commit 4976d05258 (2024-11-26 15:57 UTC)
Build Info:
  Official https://julialang.org/ release
Platform Info:
  OS: Windows (x86_64-w64-mingw32)
  CPU: 16 × 12th Gen Intel(R) Core(TM) i7-12650H
  WORD_SIZE: 64
  LIBM: libopenlibm
  LLVM: libLLVM-15.0.7 (ORCJIT, alderlake)
Threads: 1 default, 0 interactive, 1 GC (on 16 virtual cores)

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@ParamThakkar123