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energies: adding energy loss functions #734

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1c128ee
asl: adding additional symbolic loss functions
drsk0 Aug 30, 2023
9af3137
rebase: fixes
drsk0 Jan 18, 2024
1bef679
package upgrade
drsk0 Jan 27, 2024
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4 changes: 4 additions & 0 deletions Project.toml
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Original file line number Diff line number Diff line change
Expand Up @@ -19,15 +19,18 @@ Flux = "587475ba-b771-5e3f-ad9e-33799f191a9c"
ForwardDiff = "f6369f11-7733-5829-9624-2563aa707210"
Functors = "d9f16b24-f501-4c13-a1f2-28368ffc5196"
Integrals = "de52edbc-65ea-441a-8357-d3a637375a31"
JuliaFormatter = "98e50ef6-434e-11e9-1051-2b60c6c9e899"
LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
LogDensityProblems = "6fdf6af0-433a-55f7-b3ed-c6c6e0b8df7c"
Lux = "b2108857-7c20-44ae-9111-449ecde12c47"
MCMCChains = "c7f686f2-ff18-58e9-bc7b-31028e88f75d"
ModelingToolkit = "961ee093-0014-501f-94e3-6117800e7a78"
MonteCarloMeasurements = "0987c9cc-fe09-11e8-30f0-b96dd679fdca"
OhMyREPL = "5fb14364-9ced-5910-84b2-373655c76a03"
Optim = "429524aa-4258-5aef-a3af-852621145aeb"
Optimisers = "3bd65402-5787-11e9-1adc-39752487f4e2"
Optimization = "7f7a1694-90dd-40f0-9382-eb1efda571ba"
OptimizationOptimisers = "42dfb2eb-d2b4-4451-abcd-913932933ac1"
QuasiMonteCarlo = "8a4e6c94-4038-4cdc-81c3-7e6ffdb2a71b"
Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
Reexport = "189a3867-3050-52da-a836-e630ba90ab69"
Expand All @@ -37,6 +40,7 @@ Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
StochasticDiffEq = "789caeaf-c7a9-5a7d-9973-96adeb23e2a0"
SymbolicUtils = "d1185830-fcd6-423d-90d6-eec64667417b"
Symbolics = "0c5d862f-8b57-4792-8d23-62f2024744c7"
TerminalPager = "0c614874-6106-40ed-a7c2-2f1cd0bff883"
UnPack = "3a884ed6-31ef-47d7-9d2a-63182c4928ed"
Zygote = "e88e6eb3-aa80-5325-afca-941959d7151f"

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51 changes: 39 additions & 12 deletions src/adaptive_losses.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -15,6 +15,7 @@ end
"""
```julia
NonAdaptiveLoss{T}(; pde_loss_weights = 1,
asl_loss_weights = 1,
bc_loss_weights = 1,
additional_loss_weights = 1)
```
Expand All @@ -24,31 +25,34 @@ change during optimization
"""
mutable struct NonAdaptiveLoss{T <: Real} <: AbstractAdaptiveLoss
pde_loss_weights::Vector{T}
asl_loss_weights::Vector{T}
bc_loss_weights::Vector{T}
additional_loss_weights::Vector{T}
SciMLBase.@add_kwonly function NonAdaptiveLoss{T}(; pde_loss_weights = 1,
asl_loss_weights = 1,
bc_loss_weights = 1,
additional_loss_weights = 1) where {
T <:
Real
}
new(vectorify(pde_loss_weights, T), vectorify(bc_loss_weights, T),
new(vectorify(pde_loss_weights, T), vectorify(asl_loss_weights, T), vectorify(bc_loss_weights, T),
vectorify(additional_loss_weights, T))
end
end

# default to Float64
SciMLBase.@add_kwonly function NonAdaptiveLoss(; pde_loss_weights = 1, bc_loss_weights = 1,
SciMLBase.@add_kwonly function NonAdaptiveLoss(; pde_loss_weights = 1, asl_loss_weights = 1, bc_loss_weights = 1,
additional_loss_weights = 1)
NonAdaptiveLoss{Float64}(; pde_loss_weights = pde_loss_weights,
asl_loss_weights = asl_loss_weights,
bc_loss_weights = bc_loss_weights,
additional_loss_weights = additional_loss_weights)
end

function generate_adaptive_loss_function(pinnrep::PINNRepresentation,
adaloss::NonAdaptiveLoss,
pde_loss_functions, bc_loss_functions)
function null_nonadaptive_loss(θ, pde_losses, bc_losses)
pde_loss_functions, asl_loss_functions, bc_loss_functions)
function null_nonadaptive_loss(θ, pde_loss, asl_loss, bc_losses)
nothing
end
end
Expand All @@ -58,6 +62,7 @@ end
GradientScaleAdaptiveLoss(reweight_every;
weight_change_inertia = 0.9,
pde_loss_weights = 1,
asl_loss_weights = 1,
bc_loss_weights = 1,
additional_loss_weights = 1)
```
Expand Down Expand Up @@ -90,30 +95,34 @@ mutable struct GradientScaleAdaptiveLoss{T <: Real} <: AbstractAdaptiveLoss
reweight_every::Int64
weight_change_inertia::T
pde_loss_weights::Vector{T}
asl_loss_weights::Vector{T}
bc_loss_weights::Vector{T}
additional_loss_weights::Vector{T}
SciMLBase.@add_kwonly function GradientScaleAdaptiveLoss{T}(reweight_every;
weight_change_inertia = 0.9,
pde_loss_weights = 1,
asl_loss_weights = 1,
bc_loss_weights = 1,
additional_loss_weights = 1) where {
T <:
Real
}
new(convert(Int64, reweight_every), convert(T, weight_change_inertia),
vectorify(pde_loss_weights, T), vectorify(bc_loss_weights, T),
vectorify(additional_loss_weights, T))
vectorify(pde_loss_weights, T), vectorify(asl_loss_weights, T),
vectorify(bc_loss_weights, T), vectorify(additional_loss_weights, T))
end
end
# default to Float64
SciMLBase.@add_kwonly function GradientScaleAdaptiveLoss(reweight_every;
weight_change_inertia = 0.9,
pde_loss_weights = 1,
asl_loss_weights = 1,
bc_loss_weights = 1,
additional_loss_weights = 1)
GradientScaleAdaptiveLoss{Float64}(reweight_every;
weight_change_inertia = weight_change_inertia,
pde_loss_weights = pde_loss_weights,
asl_loss_weights = asl_loss_weights,
bc_loss_weights = bc_loss_weights,
additional_loss_weights = additional_loss_weights)
end
Expand All @@ -136,7 +145,7 @@ function generate_adaptive_loss_function(pinnrep::PINNRepresentation,

nonzero_divisor_eps = adaloss_T isa Float64 ? Float64(1e-11) :
convert(adaloss_T, 1e-7)
bc_loss_weights_proposed = pde_grads_max ./
bc_loss_weights_proposed = pde_asl_grads_max ./
(bc_grads_mean .+ nonzero_divisor_eps)
adaloss.bc_loss_weights .= weight_change_inertia .*
adaloss.bc_loss_weights .+
Expand All @@ -160,8 +169,10 @@ end
```julia
function MiniMaxAdaptiveLoss(reweight_every;
pde_max_optimiser = Flux.ADAM(1e-4),
asl_max_optimiser = Flux.ADAM(1e-4),
bc_max_optimiser = Flux.ADAM(0.5),
pde_loss_weights = 1,
asl_loss_weights = 1,
bc_loss_weights = 1,
additional_loss_weights = 1)
```
Expand Down Expand Up @@ -191,65 +202,81 @@ https://arxiv.org/abs/2009.04544
"""
mutable struct MiniMaxAdaptiveLoss{T <: Real,
PDE_OPT <: Flux.Optimise.AbstractOptimiser,
ASL_OPT <: Flux.Optimise.AbstractOptimiser,
BC_OPT <: Flux.Optimise.AbstractOptimiser} <:
AbstractAdaptiveLoss
reweight_every::Int64
pde_max_optimiser::PDE_OPT
asl_max_optimiser::ASL_OPT
bc_max_optimiser::BC_OPT
pde_loss_weights::Vector{T}
asl_loss_weights::Vector{T}
bc_loss_weights::Vector{T}
additional_loss_weights::Vector{T}
SciMLBase.@add_kwonly function MiniMaxAdaptiveLoss{T,
PDE_OPT, BC_OPT}(reweight_every;
PDE_OPT, ASL_OPT, BC_OPT}(reweight_every;
pde_max_optimiser = Flux.ADAM(1e-4),
asl_max_optimiser = Flux.ADAM(1e-4),
bc_max_optimiser = Flux.ADAM(0.5),
pde_loss_weights = 1,
asl_loss_weights = 1,
bc_loss_weights = 1,
additional_loss_weights = 1) where {
T <:
Real,
PDE_OPT <:
Flux.Optimise.AbstractOptimiser,
ASL_OPT <:
Flux.Optimise.AbstractOptimiser,
BC_OPT <:
Flux.Optimise.AbstractOptimiser
}
new(convert(Int64, reweight_every), convert(PDE_OPT, pde_max_optimiser),
new(convert(Int64, reweight_every), convert(PDE_OPT, pde_max_optimiser), convert(ASL_OPT, asl_max_optimiser),
convert(BC_OPT, bc_max_optimiser),
vectorify(pde_loss_weights, T), vectorify(bc_loss_weights, T),
vectorify(additional_loss_weights, T))
vectorify(asl_loss_weights, T), vectorify(additional_loss_weights, T))
end
end

# default to Float64, ADAM, ADAM
SciMLBase.@add_kwonly function MiniMaxAdaptiveLoss(reweight_every;
pde_max_optimiser = Flux.ADAM(1e-4),
asl_max_optimiser = Flux.ADAM(1e-4),
bc_max_optimiser = Flux.ADAM(0.5),
pde_loss_weights = 1,
asl_loss_weights = 1,
bc_loss_weights = 1,
additional_loss_weights = 1)
MiniMaxAdaptiveLoss{Float64, typeof(pde_max_optimiser),
typeof(bc_max_optimiser)}(reweight_every;
pde_max_optimiser = pde_max_optimiser,
asl_max_optimiser = asl_max_optimiser,
bc_max_optimiser = bc_max_optimiser,
pde_loss_weights = pde_loss_weights,
asl_loss_weights = asl_loss_weights,
bc_loss_weights = bc_loss_weights,
additional_loss_weights = additional_loss_weights)
end

function generate_adaptive_loss_function(pinnrep::PINNRepresentation,
adaloss::MiniMaxAdaptiveLoss,
pde_loss_functions, bc_loss_functions)
pde_loss_functions, asl_loss_functions, bc_loss_functions)
pde_max_optimiser = adaloss.pde_max_optimiser
asl_max_optimiser = adaloss.asl_max_optimiser
bc_max_optimiser = adaloss.bc_max_optimiser
iteration = pinnrep.iteration

function run_minimax_adaptive_loss(θ, pde_losses, bc_losses)
function run_minimax_adaptive_loss(θ, pde_losses, asl_losses, bc_losses)
if iteration[1] % adaloss.reweight_every == 0
Flux.Optimise.update!(pde_max_optimiser, adaloss.pde_loss_weights,
-pde_losses)
Flux.Optimise.update!(asl_max_optimiser, adaloss.asl_loss_weights,
-asl_losses)
Flux.Optimise.update!(bc_max_optimiser, adaloss.bc_loss_weights, -bc_losses)
logvector(pinnrep.logger, adaloss.pde_loss_weights,
"adaptive_loss/pde_loss_weights", iteration[1])
logvector(pinnrep.logger, adaloss.asl_loss_weights,
"adaptive_loss/asl_loss_weights", iteration[1])
logvector(pinnrep.logger, adaloss.bc_loss_weights,
"adaptive_loss/bc_loss_weights",
iteration[1])
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