format

README.md

@@ -27,7 +27,6 @@ For information on using the package,
 [in-development documentation](https://docs.sciml.ai/NeuralPDE/dev/) for the version of
 the documentation, which contains the unreleased features.
 
-
 ## Features
 
   - Physics-Informed Neural Networks for ODE, SDE, RODE, and PDE solving

src/NeuralPDE.jl

@@ -52,7 +52,8 @@ export NNODE, TerminalPDEProblem, NNPDEHan, NNPDENS, NNRODE,
        KolmogorovPDEProblem, NNKolmogorov, NNStopping, ParamKolmogorovPDEProblem,
        KolmogorovParamDomain, NNParamKolmogorov,
        PhysicsInformedNN, discretize,
-       GridTraining, StochasticTraining, QuadratureTraining, QuasiRandomTraining, WeightedIntervalTraining, 
+       GridTraining, StochasticTraining, QuadratureTraining, QuasiRandomTraining,
+       WeightedIntervalTraining,
        build_loss_function, get_loss_function,
        generate_training_sets, get_variables, get_argument, get_bounds,
        get_phi, get_numeric_derivative, get_numeric_integral,

src/ode_solve.jl

@@ -284,7 +284,8 @@ function generate_loss(strategy::StochasticTraining, phi, f, autodiff::Bool, tsp
     optf = OptimizationFunction(loss, Optimization.AutoZygote())
 end
 
-function generate_loss(strategy::WeightedIntervalTraining, phi, f, autodiff::Bool, tspan, p, batch)
+function generate_loss(strategy::WeightedIntervalTraining, phi, f, autodiff::Bool, tspan, p,
+                       batch)
     minT = tspan[1]
     maxT = tspan[2]
 
@@ -297,12 +298,13 @@ function generate_loss(strategy::WeightedIntervalTraining, phi, f, autodiff::Boo
 
     data = Float64[]
     for (index, item) in enumerate(weights)
-        temp_data = rand(1, trunc(Int, samples * item)) .* difference .+ minT .+ ((index - 1) * difference)
+        temp_data = rand(1, trunc(Int, samples * item)) .* difference .+ minT .+
+                    ((index - 1) * difference)
         data = append!(data, temp_data)
     end
 
     ts = data
-    
+
     function loss(θ, _)
         if batch
             sum(abs2, inner_loss(phi, f, autodiff, ts, θ, p))
@@ -413,7 +415,7 @@ function DiffEqBase.__solve(prob::DiffEqBase.AbstractODEProblem,
         verbose && println("Current loss is: $l, Iteration: $iteration")
         l < abstol
     end
-    
+
     optprob = OptimizationProblem(optf, init_params)
     res = solve(optprob, opt; callback, maxiters, alg.kwargs...)
 

src/training_strategies.jl

@@ -299,7 +299,6 @@ function get_loss_function(loss_function, lb, ub, eltypeθ, strategy::Quadrature
     return loss
 end
 
-
 """
 ```julia
 WeightedIntervalTraining(weights, samples)
@@ -328,7 +327,8 @@ function WeightedIntervalTraining(weights, samples)
     WeightedIntervalTraining(weights, samples)
 end
 
-function get_loss_function(loss_function, train_set, eltypeθ, strategy::WeightedIntervalTraining;
+function get_loss_function(loss_function, train_set, eltypeθ,
+                           strategy::WeightedIntervalTraining;
                            τ = nothing)
     loss = (θ) -> mean(abs2, loss_function(train_set, θ))
-end
+end

test/NNODE_tests.jl

@@ -218,12 +218,13 @@ true_sol = solve(prob_oop, Tsit5(), saveat = 0.01)
 func = Lux.σ
 N = 12
 chain = Lux.Chain(Lux.Dense(1, N, func), Lux.Dense(N, N, func), Lux.Dense(N, N, func),
-                    Lux.Dense(N, N, func), Lux.Dense(N, length(u0)))
+                  Lux.Dense(N, N, func), Lux.Dense(N, length(u0)))
 
 opt = Optimisers.Adam(0.01)
 weights = [0.7, 0.2, 0.1]
 samples = 200
-alg = NeuralPDE.NNODE(chain, opt, autodiff = false, strategy = NeuralPDE.WeightedIntervalTraining(weights, samples))
-sol = solve(prob_oop, alg, verbose=true, maxiters = 100000, saveat = 0.01)
+alg = NeuralPDE.NNODE(chain, opt, autodiff = false,
+                      strategy = NeuralPDE.WeightedIntervalTraining(weights, samples))
+sol = solve(prob_oop, alg, verbose = true, maxiters = 100000, saveat = 0.01)
 
-@test abs(mean(sol) - mean(true_sol)) < 0.2
+@test abs(mean(sol) - mean(true_sol)) < 0.2