implement DeepONet, refactor pinoode

src/NeuralPDE.jl

@@ -48,6 +48,7 @@ include("adaptive_losses.jl")
 include("ode_solve.jl")
 # include("rode_solve.jl")
 include("dae_solve.jl")
+include("neural_operators.jl")
 include("pino_ode_solve.jl")
 include("transform_inf_integral.jl")
 include("discretize.jl")
@@ -58,7 +59,7 @@ include("PDE_BPINN.jl")
 include("dgm.jl")
 
 
-export NNODE, NNDAE, PINOODE, TRAINSET, EquationSolving, OperatorLearning
+export NNODE, NNDAE, PINOODE, DeepONet
  PhysicsInformedNN, discretize,
  GridTraining, StochasticTraining, QuadratureTraining, QuasiRandomTraining,
  WeightedIntervalTraining,

src/neural_operators.jl

@@ -0,0 +1,47 @@
+#TODO: Add docstrings
+"""
+DeepONet(branch,trunk)
+"""
+struct DeepONet{} <: Lux.AbstractExplicitLayer
+ branch::Lux.AbstractExplicitLayer
+ trunk::Lux.AbstractExplicitLayer
+end
+
+function Lux.setup(rng::AbstractRNG, l::DeepONet)
+ branch, trunk = l.branch, l.trunk
+ θ_branch, st_branch = Lux.setup(rng, branch)
+ θ_trunk, st_trunk = Lux.setup(rng, trunk)
+ θ = (branch = θ_branch, trunk = θ_trunk)
+ st = (branch = st_branch, trunk = st_trunk)
+ θ, st
+end
+
+# function Lux.initialparameters(rng::AbstractRNG, e::DeepONet)
+# code
+# end
+
+Lux.initialstates(::AbstractRNG, ::DeepONet) = NamedTuple()
+
+"""
+example:
+
+branch = Lux.Chain(Lux.Dense(1, 32, Lux.σ), Lux.Dense(32, 1))
+trunk = Lux.Chain(Lux.Dense(1, 32, Lux.σ), Lux.Dense(32, 1))
+a = rand(1, 100, 10)
+t = rand(1, 1, 10)
+x = (branch = a, trunk = t)
+
+deeponet = DeepONet(branch, trunk)
+θ, st = Lux.setup(Random.default_rng(), deeponet)
+y = deeponet(x, θ, st)
+"""
+@inline function (f::DeepONet)(x::NamedTuple, θ, st::NamedTuple)
+ parameters, cord = x.branch, x.trunk
+ branch, trunk = f.branch, f.trunk
+ st_branch, st_trunk = st.branch, st.trunk
+ θ_branch, θ_trunk = θ.branch, θ.trunk
+ out_b, st_b = branch(parameters, θ_branch, st_branch)
+ out_t, st_t = trunk(cord, θ_trunk, st_trunk)
+ out = out_b' * out_t
+ return out, (branch = st_b, trunk = st_t)
+end