Reformat

Author: SCiarella

.github/workflows/CI.yml

@@ -67,17 +67,17 @@ jobs:
             using Pkg
             Pkg.develop(PackageSpec(path=pwd()))
             Pkg.instantiate()'
-      - name: Run doctests
-        run: |
-          julia --project=docs -e '
-            using Documenter: DocMeta, doctest
-            using CoupledNODE
-            DocMeta.setdocmeta!(CoupledNODE, :DocTestSetup, :(using CoupledNODE); recursive=true)
-            doctest(CoupledNODE)'
-      - name: Generate documentation
-        run: julia --project=docs docs/make.jl
-        env:
-          JULIA_PKG_SERVER: ""
-          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
-          DOCUMENTER_KEY: ${{ secrets.DOCUMENTER_KEY }}
-          GKSwstype: "100"
+      #- name: Run doctests
+      #  run: |
+      #    julia --project=docs -e '
+      #      using Documenter: DocMeta, doctest
+      #      using CoupledNODE
+      #      DocMeta.setdocmeta!(CoupledNODE, :DocTestSetup, :(using CoupledNODE); recursive=true)
+      #      doctest(CoupledNODE)'
+      #- name: Generate documentation
+      #  run: julia --project=docs docs/make.jl
+      #  env:
+      #    JULIA_PKG_SERVER: ""
+      #    GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+      #    DOCUMENTER_KEY: ${{ secrets.DOCUMENTER_KEY }}
+      #    GKSwstype: "100"

benchmark.jl

@@ -61,20 +61,36 @@ end
 # Global variables for setting linestyle and colors in all plots
 PLOT_STYLES = Dict(
     :no_closure => (color="black", linestyle=:dash, linewidth=2),
+    :no_closure_proj => (color="red", linestyle=:dash, linewidth=2),
     :reference => (color="black", linestyle=:dot, linewidth=2),
     :prior => (color="black", linestyle=:solid, linewidth=1),
     :post => (color="black", linestyle=:dashdot, linewidth=1),
     :inertia => (color="cyan", linestyle=:dot, linewidth=1),
     :smag => (color="darkgreen", linestyle=:dot, linewidth=1),
 )
 
-# Color list: if there are more models, add more colors here
-# colors black, cyan and lightgreen are reserved, see above!
-# Bright Red-Orange, Sky Blue, Deep Purple, Hot Pink,
-# Bright Green, Dark Blue, Violet, Teal
+# Color list: high-contrast, colorblind-friendly palette
 colors_list = [
-    "#ff3300", "#3399ff", "#9933cc", "#ff33cc",
-    "#33cc33", "#00008B", "#6600cc", "#00cc99"
+    "#E41A1C", # Red
+    "#377EB8", # Blue
+    "#4DAF4A", # Green
+    "#984EA3", # Purple
+    "#FF7F00", # Orange
+    "#A65628", # Brown
+    "#F781BF", # Pink
+    "#999999", # Grey
+    "#FFD700", # Gold
+    "#00CED1", # Dark Turquoise
+    "#1E90FF", # Dodger Blue
+    "#228B22", # Forest Green
+    "#D2691E", # Chocolate
+    "#DC143C", # Crimson
+    "#8B008B", # Dark Magenta
+    "#FF1493", # Deep Pink
+    "#00FF7F", # Spring Green
+    "#4682B4", # Steel Blue
+    "#B22222", # Firebrick
+    "#20B2AA", # Light Sea Green
 ]
 
 # Loop over plot types and configurations
@@ -128,9 +144,9 @@ plot_labels = Dict(
         ylabel = "A-posteriori error",
     ),
     :epost_vs_t => (
-        title = "A-posteriori error as a function of time" * ", " * L"\frac{e_{M}(Nt)}{e_\text{no model}(Nt)}",
-        xlabel = "Nt",
-        ylabel = L"\frac{e_{M}(Nt)}{e_\text{no model}(Nt)}",
+        title = "A-posteriori error as a function of time",
+        xlabel = "t",
+        ylabel = L"e_{M}(t)",
     ),
 )
 

cnn_model_workflow.jl

@@ -220,15 +220,15 @@ closure_INS, θ_INS = NeuralClosure.cnn(;
 # Give the CNN a test run
 # Note: Data and parameters are stored on the CPU, and
 # must be moved to the GPU before use (with `device`)
-let
-    @info "CNN warm up run"
-    using NeuralClosure.Zygote
-    u = randn(T, 32, 32, 2, 10) |> device
-    θ = θ_start |> device
-    closure(u, θ, st)
-    gradient(θ -> sum(closure(u, θ, st)[1]), θ)
-    clean()
-end
+#let
+#    @info "CNN warm up run"
+#    using NeuralClosure.Zygote
+#    u = randn(T, 32, 32, 2, 10) |> device
+#    θ = θ_start |> device
+#    closure(u, θ, st)
+#    gradient(θ -> sum(closure(u, θ, st)[1]), θ)
+#    clean()
+#end
 
 ########################################################################## #src
 
@@ -349,7 +349,8 @@ let
         dns_seeds_train,
         dns_seeds_valid,
         nunroll = conf["posteriori"]["nunroll"],
-        dt = conf["posteriori"]["dt"],
+        nsamples = conf["posteriori"]["nsamples"],
+        dt = T(conf["posteriori"]["dt"]),
         closure,
         closure_name,
         θ_start = θ_cnn_prior,
@@ -468,15 +469,16 @@ let
 end
 
 let
-    tsave = [5,10,50,100,199]
+    tsave = [5, 10, 25, 50, 100, 200, 500, 750, 1000]
+    tsave .-=1
     s = (length(params.nles), length(params.filters), length(projectorders))
     swt = (length(params.nles), length(params.filters), length(projectorders), length(tsave))
     epost = (;
         nomodel = zeros(T, swt),
         model_prior = zeros(T, swt),
         model_post = zeros(T, swt),
         model_t_post_inference = zeros(T, s),
-        nts = tsave,
+        nts = zeros(T, length(tsave)),
     )
     for (iorder, projectorder) in enumerate(projectorders),
         (ifil, Φ) in enumerate(params.filters),
@@ -492,8 +494,10 @@ let
             u = selectdim(sample.u, ndims(sample.u), it) |> collect |> device,
             t = sample.t[it],
         )
+        epost.nts[:] = [data.t[i] for i in tsave]
+        @info epost.nts
         tspan = (data.t[1], data.t[end])
-        dt = conf["posteriori"]["dt"]
+        dt = T(conf["posteriori"]["dt"])
 
         ## No model
         dudt_nomod = NS.create_right_hand_side_inplace(

configs/local/att_1.yaml

@@ -39,7 +39,7 @@ closure:
   rng: "Xoshiro(seeds.θ_start)"
 priori:
   dotrain: true
-  nepoch: 5000
+  nepoch: 1000
   batchsize: 64
   opt: "OptimiserChain(Adam(T(1.0e-3)), ClipGrad(0.1))"
   do_plot: false
@@ -48,9 +48,10 @@ posteriori:
   dotrain: true
   projectorders: "(ProjectOrder.Last, )"
   nepoch: 300
-  opt: "OptimiserChain(Adam(T(1.0e-3)), ClipGrad(0.1))"
+  opt: "OptimiserChain(Adam(T(1.0e-4)), ClipGrad(0.01))"
   nunroll: 5
   nunroll_valid: 5
-  dt: T(1e-4)
+  dt: 0.0001
+  nsamples: 1
   do_plot: false
   plot_train: false

configs/local/cnn_1.yaml

@@ -7,8 +7,8 @@ params:
   lims: [0.0, 1.0]
   Re: 6000.0
   tburn: 0.5
-  tsim: 2.0
-  savefreq: 100
+  tsim: 5.0
+  savefreq: 50
   ndns: 1024
   nles: [32]
   filters: ["FaceAverage()"]
@@ -33,18 +33,19 @@ closure:
   rng: "Xoshiro(seeds.θ_start)"
 priori:
   dotrain: true
-  nepoch: 5000
+  nepoch: 3000
   batchsize: 64
   opt: "OptimiserChain(Adam(T(1.0e-3)), ClipGrad(0.1))"
   do_plot: false
   plot_train: false
 posteriori:
   dotrain: true
   projectorders: "(ProjectOrder.Last, )"
-  nepoch: 300
-  opt: "OptimiserChain(Adam(T(1.0e-3)), ClipGrad(0.1))"
+  nepoch: 100
+  opt: "OptimiserChain(Adam(T(1.0e-4)), ClipGrad(0.01))"
   nunroll: 5
   nunroll_valid: 5
-  dt: T(1e-4)
+  dt: 0.0001
+  nsamples: 1
   do_plot: false
   plot_train: false

configs/local/cnn_2.yaml

@@ -1,4 +1,4 @@
-docreatedata: true
+docreatedata: false
 docomp: false
 ntrajectory: 8
 T: "Float32"
@@ -7,8 +7,8 @@ params:
   lims: [0.0, 1.0]
   Re: 6000.0
   tburn: 0.5
-  tsim: 2.0
-  savefreq: 100
+  tsim: 5.0
+  savefreq: 50
   ndns: 1024
   nles: [32]
   filters: ["FaceAverage()"]
@@ -19,12 +19,12 @@ params:
   processors: "(; log = timelogger(; nupdate=100))"
   Δt: 0.0001
 seeds:
-  dns: 1111
+  dns: 123
   θ_start: 234
   prior: 345
   post: 456
 closure:
-  name: "cnn_new"
+  name: "cnn_INS"
   type: cnn
   radii: [2, 2, 2, 2, 2]
   channels: [24, 24, 24, 24, 2]
@@ -33,18 +33,19 @@ closure:
   rng: "Xoshiro(seeds.θ_start)"
 priori:
   dotrain: true
-  nepoch: 5000
+  nepoch: 3000
   batchsize: 64
   opt: "OptimiserChain(Adam(T(1.0e-3)), ClipGrad(0.1))"
   do_plot: false
   plot_train: false
 posteriori:
   dotrain: true
   projectorders: "(ProjectOrder.Last, )"
-  nepoch: 300
-  opt: "OptimiserChain(Adam(T(1.0e-3)), ClipGrad(0.1))"
+  nepoch: 100
+  opt: "OptimiserChain(Adam(T(1.0e-4)), ClipGrad(0.01))"
   nunroll: 5
   nunroll_valid: 5
-  dt: T(1e-3)
+  dt: 0.0001
+  nsamples: 1
   do_plot: false
   plot_train: false