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 · 2025-02-16T16:51:46Z

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 · 2025-02-21T08:37:19Z

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

So I must remove them, right ??

ParamThakkar123 · 2025-02-22T14:22:39Z

@ChrisRackauckas Any further code changes required here ?? Are we ready to merge this ??

ChrisRackauckas · 2025-02-22T16:49:15Z

That plot hasn't changed.

ParamThakkar123 · 2025-02-22T16:50:40Z

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

Yeah. So What parameters should I consider for making the changes exactly ??

ParamThakkar123 · 2025-02-22T16:51:26Z

The times array ?? or should I remove the outliers ?? or some algorithms ?

ChrisRackauckas · 2025-02-22T16:51:49Z

The previous version had time caps to fix this. They should probably be re-added.

ParamThakkar123 · 2025-02-22T17:06:31Z

Dict{Any, Any} with 14 entries:
"32" => [0.0336096, 0.124406]
"12" => [8.69697, 9.07318]
"41" => [0.0038361, 0.0086331]
"11" => [115.273, 115.443]
"22" => [0.0079326, 0.114546]
"51" => [0.0037149, 0.008581]
"31" => [0.0042777, 0.0096933]
"61" => [0.0062327, 0.0128282]
"42" => [0.0071377, 0.0256434]
"62" => [0.0254246, 0.145936, 0.199219, 0.222898]
"52" => [0.0057367, 0.0159064, 0.0257179, 0.1677, 0.175674, 0.225474, 0.29907…
"21" => [0.0050241, 0.0088035]
"72" => [0.0043152, 0.137986, 0.261313, 0.279204, 0.307742, 0.319465, 0.33178…
"71" => [0.0051946, 0.520155, 0.525374, 0.529693, 0.535312, 0.5403, 0.56997, …

@ChrisRackauckas This is the new output after adding time caps

ChrisRackauckas · 2025-02-22T17:12:13Z

Is there something under the legend? Move it out

ParamThakkar123 · 2025-02-22T17:12:18Z

Oh! I got why this was happening, actually the code is correct but the graphs were appearing too wonky or small due to the number of iterations being performed

ParamThakkar123 · 2025-02-22T17:12:56Z

When I reduced the iterations to 100 this is what I got

Which I think looks reasonable

ParamThakkar123 · 2025-02-22T17:13:47Z

@ChrisRackauckas Your views on this ? Is this how it should be ??

ParamThakkar123 · 2025-02-22T17:24:10Z

@ChrisRackauckas Could you please review ??

ChrisRackauckas · 2025-02-22T18:59:05Z

Can you log the t there?

ParamThakkar123 · 2025-02-22T19:11:14Z

Can you log the t there?

Added the code to log t for each algorithm

ParamThakkar123 · 2025-02-24T15:06:55Z

@ChrisRackauckas All checks have passed the graph still appears the same though 😅 . Could you please review ?? I have logged t there like you said

ParamThakkar123 · 2025-02-25T14:06:31Z

@ChrisRackauckas All checks have passed the graph still appears the same though 😅

@ChrisRackauckas probably the graph is a bit zoomed out ??

ParamThakkar123 · 2025-02-26T17:41:51Z

@ChrisRackauckas Further changes to the code doesn't seem to change the graph much. Any more tweaks you might suggest ??

ParamThakkar123 · 2025-02-26T17:44:33Z

rest the code works fine and updated to the latest bump

ParamThakkar123 · 2025-02-26T17:53:03Z

Are we ready to merge this ??

ParamThakkar123 · 2025-02-27T13:06:15Z

@ChrisRackauckas Could you please review ??

@ChrisRackauckas @avik-pal ???

ChrisRackauckas · 2025-03-01T23:36:33Z

You keep pinging but the plot still isn't fixed.

ParamThakkar123 · 2025-03-02T04:04:03Z

You keep pinging but the plot still isn't fixed.

Yes. The plot isn't fixed. The reason why I was pinging was to know if there are some alternate methods to fix this, because I have tried a lot of variations of this still observed no significant changes. And every push I make on the repo, takes 48+ hours to pass through the CI and the results turn out to be something I didn't expect or get when I ran them locally.

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