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cnn_model_workflow.jl
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#! format: off
if false #src
include("src/Benchmark.jl") #src
end #src
@info "Script started"
@info VERSION
using Pkg
@info Pkg.status()
# Color palette for consistent theme throughout paper
palette = (; color = ["#3366cc", "#cc0000", "#669900", "#ff9900"])
########################################################################## #src
# Read the configuration file
using IncompressibleNavierStokes
using NeuralClosure
using CoupledNODE
using Lux
using LuxCUDA
NS = Base.get_extension(CoupledNODE, :NavierStokes)
function load_config()
if haskey(ENV, "CONF_FILE")
@info "Reading configuration file from ENV:" ENV["CONF_FILE"]
return NS.read_config(ENV["CONF_FILE"])
elseif length(ARGS) > 0
@info "Reading configuration file from ARGS:" ARGS[1]
return NS.read_config(ARGS[1])
else
@info "Reading default configuration file: configs/conf_3.yaml"
return NS.read_config("configs/conf_3.yaml")
end
end
conf = load_config()
########################################################################## #src
# Choose where to put output
basedir = haskey(ENV, "DEEPDIP") ? ENV["DEEPDIP"] : @__DIR__
outdir = joinpath(basedir, "output", "kolmogorov")
closure_name = conf["closure"]["name"]
outdir_model = joinpath(outdir, closure_name)
plotdir = joinpath(outdir, "plots", closure_name)
logdir = joinpath(outdir, "logs", closure_name)
ispath(outdir) || mkpath(outdir)
ispath(outdir_model) || mkpath(outdir_model)
ispath(plotdir) || mkpath(plotdir)
ispath(logdir) || mkpath(logdir)
# Turn off plots for array jobs.
# If all the workers do this at the same time, one might
# error when saving the file at the same time
doplot() = true
########################################################################## #src
# ## Configure logger ``
using Benchmark
using Dates
# Write output to file, as the default SLURM file is not updated often enough
isslurm = haskey(ENV, "SLURM_JOB_ID")
if isslurm
jobid = parse(Int, ENV["SLURM_JOB_ID"])
taskid = parse(Int, ENV["SLURM_ARRAY_TASK_ID"])
numtasks = parse(Int, ENV["SLURM_ARRAY_TASK_COUNT"])
logfile = "job=$(jobid)_task=$(taskid)_$(Dates.now()).out"
else
taskid = 1
numtasks = 1
logfile = "log_$(Dates.now()).out"
end
logfile = joinpath(logdir, logfile)
setsnelliuslogger(logfile)
@info "# A-posteriori analysis: Forced turbulence (2D)"
# ## Load packages
@info "Loading packages"
if "CoupledNODE" in keys(Pkg.installed())
@info "CoupledNODE already installed"
else
Pkg.add(PackageSpec(rev = "main", url = "https://github.com/DEEPDIP-project/CoupledNODE.jl.git"))
end
using Accessors
using Adapt
using CairoMakie
using CoupledNODE: loss_priori_lux, create_loss_post_lux
using CUDA
using DifferentialEquations
using IncompressibleNavierStokes.RKMethods
using JLD2
using LaTeXStrings
using LinearAlgebra
using Lux
using LuxCUDA
using NNlib
using Optimisers
using ParameterSchedulers
using Random
using SparseArrays
# ## Random number seeds
#
# Use a new RNG with deterministic seed for each code "section"
# so that e.g. training batch selection does not depend on whether we
# generated fresh filtered DNS data or loaded existing one (the
# generation of which would change the state of a global RNG).
#
# Note: Using `rng = Random.default_rng()` twice seems to point to the
# same RNG, and mutating one also mutates the other.
# `rng = Xoshiro()` creates an independent copy each time.
#
# We define all the seeds here.
seeds = NS.load_seeds(conf)
########################################################################## #src
# ## Hardware selection
# Precision
T = eval(Meta.parse(conf["T"]))
# Device
if CUDA.functional()
## For running on a CUDA compatible GPU
@info "Running on CUDA"
cuda_active = true
backend = CUDABackend()
CUDA.allowscalar(false)
device = x -> adapt(CuArray, x)
clean() = (GC.gc(); CUDA.reclaim())
else
## For running on CPU.
## Consider reducing the sizes of DNS, LES, and CNN layers if
## you want to test run on a laptop.
@warn "Running on CPU"
cuda_active = false
backend = CPU()
device = identity
clean() = nothing
end
conf["params"]["backend"] = deepcopy(backend)
@info backend
########################################################################## #src
# ## Data generation
#
# Create filtered DNS data for training, validation, and testing.
# Parameters
params = NS.load_params(conf)
# DNS seeds
ntrajectory = conf["ntrajectory"]
dns_seeds = splitseed(seeds.dns, ntrajectory)
dns_seeds_train = dns_seeds[1:ntrajectory-2]
dns_seeds_valid = dns_seeds[ntrajectory-1:ntrajectory-1]
dns_seeds_test = dns_seeds[ntrajectory:ntrajectory]
# Create data
docreatedata = conf["docreatedata"]
for i = 1:ntrajectory
if i%numtasks == taskid - 1
docreatedata && createdata(; params, seed = dns_seeds[i], outdir, backend)
end
end
@info "Data generated"
# Computational time
docomp = conf["docomp"]
docomp && let
comptime, datasize = 0.0, 0.0
for seed in dns_seeds
comptime += load(
getdatafile(outdir, params.nles[1], params.filters[1], seed),
"comptime",
)
end
for seed in dns_seeds, nles in params.nles, Φ in params.filters
data = namedtupleload(getdatafile(outdir, nles, Φ, seed))
datasize += Base.summarysize(data)
end
@info "Data" comptime
@info "Data" comptime / 60 datasize * 1e-9
clean()
end
# LES setups
setups = map(nles -> getsetup(; params, nles), params.nles);
########################################################################## #src
# ## CNN closure model
# All training sessions will start from the same θ₀
# for a fair comparison.
closure, θ_start, st = NS.load_model(conf)
# Get the same model structure in INS format
closure_INS, θ_INS = NeuralClosure.cnn(;
setup = setups[1],
radii = conf["closure"]["radii"],
channels = conf["closure"]["channels"],
activations = [eval(Meta.parse(func)) for func in conf["closure"]["activations"]],
use_bias = conf["closure"]["use_bias"],
rng = eval(Meta.parse(conf["closure"]["rng"])),
)
@assert device(θ_start) == device(θ_INS)
@info "Initialized CNN with $(length(θ_start)) parameters"
# 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
########################################################################## #src
# ## Training
# ### A-priori training
#
# Train one set of CNN parameters for each of the filter types and grid sizes.
# Use the same batch selection random seed for each training setup.
# Save parameters to disk after each run.
# Plot training progress (for a validation data batch).
# Train
for i = 1:ntrajectory
if i%numtasks == taskid -1
let
dotrain = conf["priori"]["dotrain"]
nepoch = conf["priori"]["nepoch"]
dotrain && trainprior(;
params,
priorseed = seeds.prior,
dns_seeds_train,
dns_seeds_valid,
taskid = i,
outdir,
plotdir,
closure,
closure_name,
θ_start,
st,
opt = eval(Meta.parse(conf["priori"]["opt"])),
batchsize = conf["priori"]["batchsize"],
do_plot = conf["priori"]["do_plot"],
plot_train = conf["priori"]["plot_train"],
nepoch,
)
end
end
end
# Load learned parameters and training times
priortraining = loadprior(outdir, closure_name, params.nles, params.filters)
θ_cnn_prior = map(p -> copyto!(copy(θ_start), p.θ), priortraining)
@info "" θ_cnn_prior .|> extrema # Check that parameters are within reasonable bounds
# Training times
map(p -> p.comptime, priortraining)
map(p -> p.comptime, priortraining) |> vec .|> x -> round(x; digits = 1)
map(p -> p.comptime, priortraining) |> sum |> x -> x / 60 # Minutes
# ## Plot training history
with_theme(; palette) do
doplot() || return
fig = Figure(; size = (950, 250))
for (ig, nles) in enumerate(params.nles)
ax = Axis(
fig[1, ig];
title = "n = $(nles)",
xlabel = "Iteration",
ylabel = "A-priori error",
ylabelvisible = ig == 1,
yticksvisible = ig == 1,
yticklabelsvisible = ig == 1,
)
ylims!(-0.05, 1.05)
lines!(
ax,
# [Point2f(0, 1), Point2f(priortraining[ig, 1].lhist_nomodel[end][1], 1)];
priortraining[ig, 1].lhist_nomodel,
label = "No closure",
linestyle = :dash,
)
for (ifil, Φ) in enumerate(params.filters)
label = Φ isa FaceAverage ? "FA" : "VA"
lines!(ax, priortraining[ig, ifil].lhist_val; label)
end
end
axes = filter(x -> x isa Axis, fig.content)
linkaxes!(axes...)
Legend(fig[1, end+1], axes[1])
figdir = joinpath(plotdir, "priortraining")
ispath(figdir) || mkpath(figdir)
save("$figdir/validationerror.pdf", fig)
display(fig)
end
########################################################################## #src
# ### A-posteriori training
#
# Train one set of CNN parameters for each
# projection order, filter type and grid size.
# Use the same batch selection random seed for each training setup.
# Save parameters to disk after each combination.
# Plot training progress (for a validation data batch).
#
# [INS] The time stepper `RKProject` allows for choosing when to project.
# [CNODE] Only DCF (last) is supported since it appears to be the best one.
projectorders = eval(Meta.parse(conf["posteriori"]["projectorders"]))
nprojectorders = length(projectorders)
@assert nprojectorders == 1 "Only DCF should be done"
# Train
for i = 1:ntrajectory
if i%numtasks == taskid -1
let
dotrain = conf["posteriori"]["dotrain"]
nepoch = conf["posteriori"]["nepoch"]
dotrain && trainpost(;
params,
projectorders,
outdir,
plotdir,
taskid = i,
postseed = seeds.post,
dns_seeds_train,
dns_seeds_valid,
nunroll = conf["posteriori"]["nunroll"],
closure,
closure_name,
θ_start = θ_cnn_prior,
st,
opt = eval(Meta.parse(conf["posteriori"]["opt"])),
nunroll_valid = conf["posteriori"]["nunroll_valid"],
nepoch,
dt = eval(Meta.parse(conf["posteriori"]["dt"])),
do_plot = conf["posteriori"]["do_plot"],
plot_train = conf["posteriori"]["plot_train"],
)
end
end
end
# Load learned parameters and training times
posttraining = loadpost(outdir, closure_name, params.nles, params.filters, projectorders)
θ_cnn_post = map(p -> copyto!(copy(θ_start), p.θ), posttraining)
@info "" θ_cnn_post .|> extrema # Check that parameters are within reasonable bounds
# Training times
map(p -> p.comptime, posttraining) ./ 60
map(p -> p.comptime, posttraining) |> sum |> x -> x / 60
map(p -> p.comptime, posttraining) |> x -> reshape(x, :, nprojectorders) .|> x -> round(x; digits = 1)
# ## Plot a-posteriori training history
with_theme(; palette) do
doplot() || return
fig = Figure(; size = (950, 400))
for (iorder, projectorder) in enumerate(projectorders)
axes = []
for (ig, nles) in enumerate(params.nles)
ax = Axis(
fig[iorder, ig];
title = "n = $(nles)",
xlabel = "Iteration",
ylabel = projectorder == ProjectOrder.First ? "DIF" : "DCF",
ylabelvisible = ig == 1,
ylabelfont = :bold,
titlevisible = iorder == 1,
xlabelvisible = iorder == 2,
xticksvisible = iorder == 2,
xticklabelsvisible = iorder == 2,
)
for (ifil, Φ) in enumerate(params.filters)
postfile = Benchmark.getpostfile(outdir, closure_name, nles, Φ, projectorder)
check = namedtupleload(postfile)
# print the keys of the checkpoint
(; lhist_val) = check[1]
label = Φ isa FaceAverage ? "FA" : "VA"
lines!(ax, lhist_val; color = Cycled(ifil + 1), label)
end
ig == 4 && iorder == 1 && axislegend(ax)
push!(axes, ax)
end
# linkaxes!(axes...)
end
# axes = filter(x -> x isa Axis, fig.content)
# linkaxes!(axes...)
# Legend(fig[:, end+1], filter(x -> x isa Axis, fig.content)[1])
Label(fig[0, :], "A-posteriori error"; valign = :bottom, font = :bold)
rowgap!(fig.layout, 10)
figdir = joinpath(plotdir, "posttraining")
ispath(figdir) || mkpath(figdir)
save("$figdir/validationerror.pdf", fig)
display(fig)
end
########################################################################## #src
# ## Prediction errors
# ### Compute a-priori errors
#
# Note that it is still interesting to compute the a-priori errors for the
# a-posteriori trained CNN.
let
eprior = (;
nomodel = ones(T, length(params.nles)),
prior = zeros(T, size(θ_cnn_prior)),
post = zeros(T, size(θ_cnn_post)),
)
for (ifil, Φ) in enumerate(params.filters), (ig, nles) in enumerate(params.nles)
@info "Computing a-priori errors" Φ nles
setup = getsetup(; params, nles)
data = map(s -> namedtupleload(getdatafile(outdir, nles, Φ, s)), dns_seeds_test)
testset = create_io_arrays(data, setup)
i = 1:min(1000, size(testset.u, 4))
u, c = testset.u[:, :, :, i], testset.c[:, :, :, i]
testset = (u, c) |> device
priori_err(θ) = loss_priori_lux(closure, θ, st, testset)
eprior.prior[ig, ifil] = priori_err(device(θ_cnn_prior[ig, ifil]))[1]
for iorder in eachindex(projectorders)
eprior.post[ig, ifil, iorder] = priori_err(device(θ_cnn_post[ig, ifil, iorder]))[1]
end
end
jldsave(joinpath(outdir_model, "eprior.jld2"); eprior...)
end
clean()
eprior = namedtupleload(joinpath(outdir_model, "eprior.jld2"))
########################################################################## #src
# ### Compute a-posteriori errors
let
sample = namedtupleload(
getdatafile(outdir, params.nles[1], params.filters[1], dns_seeds_test[1]),
)
sample.t[100]
end
let
s = (length(params.nles), length(params.filters), length(projectorders))
epost = (;
nomodel = zeros(T, s),
cnn_prior = zeros(T, s),
cnn_post = zeros(T, s),
)
for (iorder, projectorder) in enumerate(projectorders),
(ifil, Φ) in enumerate(params.filters),
(ig, nles) in enumerate(params.nles)
@info "Computing a-posteriori errors" projectorder Φ nles
I = CartesianIndex(ig, ifil, iorder)
setup = getsetup(; params, nles)
psolver = psolver_spectral(setup)
sample = namedtupleload(getdatafile(outdir, nles, Φ, dns_seeds_test[1]))
it = 1:100
data = (;
u = selectdim(sample.u, ndims(sample.u), it) |> collect |> device,
t = sample.t[it],
)
dt = T(1e-3)
## No model
dudt_nomod = NS.create_right_hand_side(
setup, psolver)
err_post = create_loss_post_lux(dudt_nomod; sciml_solver = Tsit5(), dt = dt, use_cuda = CUDA.functional())
epost.nomodel[I] = err_post(closure, θ_cnn_post[I].*0 , st, data)[1]
# with closure
dudt = NS.create_right_hand_side_with_closure(
setup, psolver, closure, st)
err_post = create_loss_post_lux(dudt; sciml_solver = Tsit5(), dt = dt, use_cuda = CUDA.functional())
epost.cnn_prior[I] = err_post(closure, device(θ_cnn_prior[ig, ifil]), st, data)[1]
epost.cnn_post[I] = err_post(closure, device(θ_cnn_post[I]), st, data)[1]
clean()
end
jldsave(joinpath(outdir_model, "epost.jld2"); epost...)
end
epost = namedtupleload(joinpath(outdir_model, "epost.jld2"))
########################################################################## #src
# ### Plot a-priori errors
# Better for PDF export
CairoMakie.activate!()
with_theme(; palette) do
fig = Figure(; size = (800, 300))
axes = []
for (ifil, Φ) in enumerate(params.filters)
ax = Axis(
fig[1, ifil];
xscale = log10,
xticks = params.nles,
xlabel = "Resolution",
# title = "Relative a-priori error $(ifil == 1 ? " (FA)" : " (VA)")",
# title = "$(Φ isa FaceAverage ? "FA" : "VA")",
title = "$(Φ isa FaceAverage ? "Face-averaging" : "Volume-averaging")",
ylabel = "A-priori error",
ylabelvisible = ifil == 1,
yticksvisible = ifil == 1,
yticklabelsvisible = ifil == 1,
)
for (e, marker, label, color) in [
(eprior.nomodel, :circle, "No closure", Cycled(1)),
(eprior.prior[:, ifil], :utriangle, "CNN (prior)", Cycled(2)),
(eprior.post[:, ifil, 1], :diamond, "CNN (post, DCF)", Cycled(3)),
]
scatterlines!(params.nles, e; marker, color, label)
end
# axislegend(; position = :lb)
ylims!(ax, (T(-0.05), T(1.05)))
push!(axes, ax)
end
Legend(fig[1, end+1], axes[1])
save("$plotdir/eprior.pdf", fig)
display(fig)
end
########################################################################## #src
# ### Plot a-posteriori errors
# Better for PDF export
CairoMakie.activate!()
with_theme(; palette) do
doplot() || return
fig = Figure(; size = (800, 300))
linestyles = [:solid, :dash]
linestyles = [:solid]
for (iorder, projectorder) in enumerate(projectorders)
lesmodel = iorder == 1 ? "DCF" : "DCF"
(; nles) = params
ax = Axis(
fig[1, iorder];
xscale = log10,
yscale = log10,
xticks = nles,
xlabel = "Resolution",
title = "$lesmodel",
ylabel = "A-posteriori error",
ylabelvisible = iorder == 1,
yticksvisible = iorder == 1,
yticklabelsvisible = iorder == 1,
)
for (e, marker, label, color) in [
(epost.nomodel, :circle, "No closure", Cycled(1)),
(epost.cnn_prior, :rect, "CNN (Lprior)", Cycled(3)),
(epost.cnn_post, :diamond, "CNN (Lpost)", Cycled(4)),
]
for (ifil, linestyle) in enumerate(linestyles)
ifil == 2 && (label = nothing)
scatterlines!(nles, e[:, ifil, iorder]; color, linestyle, marker, label)
end
end
# ylims!(ax, (T(0.025), T(1.00)))
end
linkaxes!(filter(x -> x isa Axis, fig.content)...)
g = GridLayout(fig[1, end+1])
Legend(g[1, 1], filter(x -> x isa Axis, fig.content)[1]; valign = :bottom)
Legend(
g[2, 1],
map(s -> LineElement(; color = :black, linestyle = s), linestyles),
["FA"];
orientation = :horizontal,
valign = :top,
)
rowsize!(g, 1, Relative(1 / 2))
save("$plotdir/epost.pdf", fig)
display(fig)
end
########################################################################## #src
# ## Energy evolution
# ### Compute total kinetic energy as a function of time
CUDA.allowscalar() do
let
s = length(params.nles), length(params.filters), length(projectorders)
keys = [:ref, :nomodel, :cnn_prior, :cnn_post]
divergencehistory = (; map(k -> k => fill(Point2f[], s), keys)...)
energyhistory = (; map(k -> k => fill(Point2f[], s), keys)...)
for (iorder, projectorder) in enumerate(projectorders),
(ifil, Φ) in enumerate(params.filters),
(ig, nles) in enumerate(params.nles)
I = CartesianIndex(ig, ifil, iorder)
@info "Computing divergence and kinetic energy" projectorder Φ nles
setup = getsetup(; params, nles)
psolver = default_psolver(setup)
sample = namedtupleload(getdatafile(outdir, nles, Φ, dns_seeds_test[1]))
ustart = selectdim(sample.u, ndims(sample.u), 1) |> collect |> device
T = eltype(ustart)
# Shorter time for DIF
t_DIF = T(1)
# Reference trajectories
divergencehistory.ref[I] = let
div = scalarfield(setup)
udev = vectorfield(setup)
it = 1:5:length(sample.t)
map(it) do it
t = sample.t[it]
u = selectdim(sample.u, ndims(sample.u), it)
copyto!(udev, u)
IncompressibleNavierStokes.divergence!(div, udev, setup)
d = view(div, setup.grid.Ip)
d = sum(abs2, d) / length(d)
d = sqrt(d)
Point2f(t, d)
end
end
energyhistory.ref[I] = let
it = 1:5:length(sample.t)
udev = vectorfield(setup)
map(it) do it
t = sample.t[it]
u = selectdim(sample.u, ndims(sample.u), it)
copyto!(udev, u)
Point2f(t, total_kinetic_energy(udev, setup))
end
end
nupdate = 5
writer = processor() do state
div = scalarfield(setup)
dhist = Point2f[]
ehist = zeros(Point2f, 0)
on(state) do (; u, t, n)
if n % nupdate == 0
IncompressibleNavierStokes.divergence!(div, u, setup)
d = view(div, setup.grid.Ip)
d = sum(abs2, d) / length(d)
d = sqrt(d)
push!(dhist, Point2f(t, d))
e = total_kinetic_energy(u, setup)
push!(ehist, Point2f(t, e))
end
end
state[] = state[] # Compute initial divergence
(; dhist, ehist)
end
for (sym, closure_model, θ) in [
(:nomodel, nothing, nothing),
(:cnn_prior, wrappedclosure(closure_INS, setup), device(θ_cnn_prior[ig, ifil])),
(:cnn_post, wrappedclosure(closure_INS, setup), device(θ_cnn_post[I])),
]
_, results = solve_unsteady(;
setup = (; setup..., closure_model),
ustart,
tlims = (
sample.t[1],
projectorder == ProjectOrder.First ? t_DIF : sample.t[end],
),
Δt_min = T(1e-5),
method = RKProject(params.method, projectorder),
processors = (; writer, logger = timelogger(; nupdate = 500)),
psolver,
θ,
)
divergencehistory[sym][I] = results.writer.dhist
energyhistory[sym][I] = results.writer.ehist
end
end
jldsave(joinpath(outdir_model, "history.jld2"); energyhistory, divergencehistory)
clean()
end
end
(; divergencehistory, energyhistory) = namedtupleload(joinpath(outdir_model, "history.jld2"));
########################################################################## #src
# Check that energy is within reasonable bounds
energyhistory.ref .|> extrema
energyhistory.nomodel .|> extrema
energyhistory.cnn_prior .|> extrema
energyhistory.cnn_post .|> extrema
# Check that divergence is within reasonable bounds
divergencehistory.ref .|> extrema
divergencehistory.nomodel .|> extrema
divergencehistory.cnn_prior .|> extrema
divergencehistory.cnn_post .|> extrema
########################################################################## #src
# ### Plot energy evolution
# Better for PDF export
CairoMakie.activate!()
with_theme(; palette) do
doplot() || return
for (igrid, nles) in enumerate(params.nles)
@info "Plotting energy evolution" nles
fig = Figure(; size = (800, 450))
g = GridLayout(fig[1, 1])
for (iorder, projectorder) in enumerate(projectorders),
(ifil, Φ) in enumerate(params.filters)
I = CartesianIndex(igrid, ifil, iorder)
subfig = g[ifil, iorder]
ax = Axis(
subfig;
# xscale = log10,
# yscale = log10,
xlabel = "t",
# ylabel = Φ isa FaceAverage ? "Face-average" : "Volume-average",
ylabel = "E(t)",
# ylabelfont = :bold,
title = projectorder == ProjectOrder.First ? "DIF" : "DCF",
titlevisible = ifil == 1,
xlabelvisible = ifil == 2,
xticksvisible = ifil == 2,
xticklabelsvisible = ifil == 2,
ylabelvisible = iorder == 1,
yticksvisible = iorder == 1,
yticklabelsvisible = iorder == 1,
aspect = DataAspect(),
)
# xlims!(ax, (1e-2, 5.0))
# xlims!(ax, (0.0, 1.0))
# ylims!(ax, (1.3, 2.3))
plots = [
(energyhistory.nomodel, :solid, 1, "No closure"),
(energyhistory.cnn_prior, :solid, 3, "CNN (prior)"),
(energyhistory.cnn_post, :solid, 4, "CNN (post)"),
(energyhistory.ref, :dash, 1, "Reference"),
]
for (p, linestyle, i, label) in plots
lines!(ax, p[I]; color = Cycled(i), linestyle, label)
iorder == 1 && xlims!(-0.05, 1.05)
# iorder == 1 && ylims!(1.1, 3.1)
ylims!(1.3, 3.0)
end
# Plot zoom-in box
if iorder == 2
tlims = iorder == 1 ? (0.05, 0.2) : (0.8, 1.2)
i1 = findfirst(p -> p[1] > tlims[1], energyhistory.ref[I])
i2 = findfirst(p -> p[1] > tlims[2], energyhistory.ref[I])
tlims = energyhistory.ref[I][i1][1], energyhistory.ref[I][i2][1]
klims = energyhistory.ref[I][i1][2], energyhistory.ref[I][i2][2]
dk = klims[2] - klims[1]
# klims = klims[1] - 0.2 * dk, klims[2] + 0.2 * dk
w = iorder == 1 ? 0.2 : 0.1
klims = klims[1] + w * dk, klims[2] - w * dk
box = [
Point2f(tlims[1], klims[1]),
Point2f(tlims[2], klims[1]),
Point2f(tlims[2], klims[2]),
Point2f(tlims[1], klims[2]),
Point2f(tlims[1], klims[1]),
]
lines!(ax, box; color = :black)
ax2 = Axis(
subfig;
# bbox = BBox(0.8, 0.9, 0.2, 0.3),
width = Relative(0.35),
height = Relative(0.35),
halign = 0.05,
valign = 0.95,
limits = (tlims..., klims...),
xscale = log10,
yscale = log10,
xticksvisible = false,
xticklabelsvisible = false,
xgridvisible = false,
yticksvisible = false,
yticklabelsvisible = false,
ygridvisible = false,
backgroundcolor = :white,
)
# https://discourse.julialang.org/t/makie-inset-axes-and-their-drawing-order/60987/5
translate!(ax2.scene, 0, 0, 10)
translate!(ax2.elements[:background], 0, 0, 9)
for (p, linestyle, i, label) in plots
lines!(ax2, p[igrid, ifil, iorder]; color = Cycled(i), linestyle, label)
end
end
Label(
g[ifil, 0],
# Φ isa FaceAverage ? "Face-average" : "Volume-average";
Φ isa FaceAverage ? "FA" : "VA";
# halign = :right,
font = :bold,
# rotation = pi/2,
tellheight = false,
)
end
colgap!(g, 10)
rowgap!(g, 10)
# colsize!(g, 1, Relative(1 / 5))
Legend(fig[:, end+1], filter(x -> x isa Axis, fig.content)[1])
name = "$plotdir/energy_evolution/"
ispath(name) || mkpath(name)
save("$(name)/nles=$(nles).pdf", fig)
display(fig)
end
end
########################################################################## #src
# ### Plot Divergence
# Better for PDF export
CairoMakie.activate!()
with_theme(; palette) do
doplot() || return
islog = true
for (igrid, nles) in enumerate(params.nles)
@info "Plotting divergence" nles
fig = Figure(; size = (800, 450))
for (iorder, projectorder) in enumerate(projectorders),
(ifil, Φ) in enumerate(params.filters)
I = CartesianIndex(igrid, ifil, iorder)
subfig = fig[ifil, iorder]
ax = Axis(
subfig;
yscale = islog ? log10 : identity,
xlabel = "t",
ylabel = Φ isa FaceAverage ? "Face-average" : "Volume-average",
ylabelfont = :bold,
title = projectorder == ProjectOrder.First ? "DIF" : "DCF",
titlevisible = ifil == 1,
xlabelvisible = ifil == 2,
xticksvisible = ifil == 2,
xticklabelsvisible = ifil == 2,
ylabelvisible = iorder == 1,
yticksvisible = iorder == 1,
yticklabelsvisible = iorder == 1,
)
lines!(ax, divergencehistory.nomodel[I]; label = "No closure")
lines!(ax, divergencehistory.cnn_prior[I]; label = "CNN (prior)")
lines!(ax, divergencehistory.cnn_post[I]; label = "CNN (post)")
lines!(
ax,
divergencehistory.ref[I];
color = Cycled(1),
linestyle = :dash,
label = "Reference",
)
islog && ylims!(ax, (T(1e-6), T(1e3)))
iorder == 1 && xlims!(ax, (-0.05, 1.05))
end
rowgap!(fig.layout, 10)
colgap!(fig.layout, 10)
Legend(fig[:, end+1], filter(x -> x isa Axis, fig.content)[1])
name = "$plotdir/divergence/"
ispath(name) || mkpath(name)
save("$(name)/nles=$(nles).pdf", fig)
display(fig)
end
end
########################################################################## #src
# ## Solutions at different times
let
s = length(params.nles), length(params.filters), length(projectorders)
temp = zeros(T, ntuple(Returns(0), params.D + 1))
keys = [:ref, :nomodel, :cnn_prior, :cnn_post]
times = T[0.1, 0.5, 1.0, 5.0]
itime_max_DIF = 3
times_exact = copy(times)
utimes = map(t -> (; map(k -> k => fill(temp, s), keys)...), times)
for (iorder, projectorder) in enumerate(projectorders),
(ifil, Φ) in enumerate(params.filters),
(igrid, nles) in enumerate(params.nles)
@info "Computing test solutions" projectorder Φ nles
I = CartesianIndex(igrid, ifil, iorder)
setup = getsetup(; params, nles)
psolver = default_psolver(setup)
sample = namedtupleload(getdatafile(outdir, nles, Φ, dns_seeds_test[1]))
ustart = selectdim(sample.u, ndims(sample.u), 1) |> collect
t = sample.t
function solve(ustart, tlims, closure_model, θ)
result = solve_unsteady(;
setup = (; setup..., closure_model),
ustart = device(ustart),
tlims,
method = RKProject(params.method, projectorder),
psolver,
θ,
)[1].u |> Array
#@info result
#Array(result)
end
t1 = t[1]
for i in eachindex(times)
# Only first times for First
i > itime_max_DIF && projectorder == ProjectOrder.First && continue
# Time interval
t0 = t1
t1 = times[i]
# Adjust t1 to be exactly on a reference time
it = findfirst(>(t1), t)
if isnothing(it)
# Not found: Final time
it = length(t)
end
t1 = t[it]
tlims = (t0, t1)
times_exact[i] = t1
getprev(i, sym) = i == 1 ? ustart : utimes[i-1][sym][I]
# Compute fields
utimes[i].ref[I] = selectdim(sample.u, ndims(sample.u), it) |> collect
utimes[i].nomodel[I] = solve(getprev(i, :nomodel), tlims, nothing, nothing)
#utimes[i].nomodel[I,:] = solve(getprev(i, :nomodel), tlims, nothing, nothing)
utimes[i].cnn_prior[I] = solve(
getprev(i, :cnn_prior),
tlims,
wrappedclosure(closure_INS, setup),
device(θ_cnn_prior[igrid, ifil]),
)
utimes[i].cnn_post[I] = solve(
getprev(i, :cnn_post),
tlims,
wrappedclosure(closure_INS, setup),
device(θ_cnn_post[I]),
)
end
clean()
end
jldsave("$outdir_model/solutions.jld2"; u = utimes, t = times_exact, itime_max_DIF)
end;
# Load solution
solutions = namedtupleload("$outdir_model/solutions.jld2");
########################################################################## #src
# ### Plot spectra
#
# Plot kinetic energy spectra.
with_theme(; palette) do
doplot() || return
for (ifil, Φ) in enumerate(params.filters), (igrid, nles) in enumerate(params.nles)
@info "Plotting spectra" Φ nles
fig = Figure(; size = (800, 450))
fil = Φ isa FaceAverage ? "face-average" : "volume-average"
setup = getsetup(; params, nles)
(; Ip) = setup.grid
(; inds, κ, K) = IncompressibleNavierStokes.spectral_stuff(setup)
kmax = maximum(κ)
allaxes = []
for (iorder, projectorder) in enumerate(projectorders)
rowaxes = []
for (itime, t) in enumerate(solutions.t)
# Only first time for First
projectorder == ProjectOrder.First &&
itime > solutions.itime_max_DIF &&