update

.gitignore

@@ -11,3 +11,4 @@ test/test_data/*.jld2
 
 # Benchmarking
 simulations/Benchmark/output
+simulations/Benchmark/Manifest.toml

simulations/Benchmark/Project.toml

@@ -35,12 +35,12 @@ Accessors = "0.1"
 Adapt = "4"
 CUDA = "5"
 CairoMakie = "0.12"
-CoupledNODE = "0.0.2"
+CoupledNODE = "0.0"
 Dates = "1"
 DocStringExtensions = "0.9"
 EnumX = "1"
 FFTW = "1"
-IncompressibleNavierStokes = "2"
+IncompressibleNavierStokes = "^2.0.1"
 JLD2 = "0.5"
 LaTeXStrings = "1"
 LinearAlgebra = "1"
@@ -49,9 +49,9 @@ Lux = "1"
 LuxCUDA = "0.3"
 MLUtils = "0.4"
 NNlib = "0.9"
-NeuralClosure = "1"
+NeuralClosure = "1.0.0"
 Observables = "0.5"
 Optimisers = "0.3, 0.4"
 ParameterSchedulers = "0.4"
 SparseArrays = "1"
-julia = "1.9"
+julia = "1.10"

simulations/Benchmark/benchmark.jl

@@ -127,7 +127,7 @@ params = (;
     Re = T(6e3),
     tburn = T(0.5),
     tsim = T(2),
-    savefreq = 50,
+    savefreq = 100,
     ndns = 64,
     nles = [32,],
     filters = (FaceAverage(),),
@@ -137,6 +137,7 @@ params = (;
     bodyforce = (dim, x, y, t) -> (dim == 1) * 5 * sinpi(8 * y),
     issteadybodyforce = true,
     processors = (; log = timelogger(; nupdate = 100)),
+    Δt = T(1e-3),
 )
 
 # DNS seeds

simulations/Benchmark/src/train.jl

@@ -20,7 +20,7 @@ createdata(; params, seeds, outdir, taskid) =
             ispath(datadir) || mkpath(datadir)
             push!(filenames, f)
         end
-        data = create_les_data(; params..., rng = Xoshiro(seed), filenames)
+        data = create_les_data(; params..., rng = Xoshiro(seed), filenames, Δt = params.Δt)
         @info(
             "Trajectory info:",
             data[1].comptime / 60,
@@ -83,11 +83,15 @@ function trainprior(;
         figfile = joinpath(figdir, splitext(basename(priorfile))[1] * ".pdf")
         checkfile = join(splitext(priorfile), "_checkpoint")
         batchseed, validseed = splitseed(priorseed, 2) # Same seed for all training setups
-        setup = getsetup(; params, nles)
-        #data_train =
-        #    map(s -> namedtupleload(getdatafile(outdir, nles, Φ, s)), dns_seeds_train)
-        #data_valid =
-        #    map(s -> namedtupleload(getdatafile(outdir, nles, Φ, s)), dns_seeds_valid)
+
+        # Read the data in the format expected by the CoupledNODE
+        T = eltype(params.Re)
+        setup = []
+        for nl in nles
+            x = ntuple(α -> LinRange(T(0.0), T(1.0), nl + 1), params.D)
+            push!(setup, Setup(; x = x, Re = params.Re))
+        end
+
         # Read the data in the format expected by the CoupledNODE
         data_train = [] 
         for s in dns_seeds_train
@@ -99,18 +103,10 @@ function trainprior(;
             data_i = namedtupleload(getdatafile(outdir, nles, Φ, s))
             push!(data_valid, hcat(data_i))
         end
-        @show length(data_train)
-        @show typeof(data_train)
-        @assert false
-        @show size(data_train[1].u)
-        @show size(data_train[1])
         io_train = CoupledNODE.NavierStokes.create_io_arrays_priori(data_train, setup)
-        @assert false
-#        io_valid = CoupledNODE.NavierStokes.create_io_arrays_priori(data_valid, setup)
+        io_valid = CoupledNODE.NavierStokes.create_io_arrays_priori(data_valid, setup)
         θ = device(θ_start)
-        dataloader_prior = CoupledNODE.NavierStokes.create_dataloader_prior(data_train; batchsize = batchsize,rng=dns_seeds_train)
-        @info dataloader_prior()
-        @assert false
+        dataloader_prior = CoupledNODE.NavierStokes.create_dataloader_prior(io_train[1]; batchsize = batchsize,rng=Random.Xoshiro(dns_seeds_train[1]))
         train_data_priori = dataloader_prior()
         loss = loss_priori_lux(closure, θ, st, train_data_priori)
         optstate = Optimisers.setup(opt, θ)