fix typo

SciML · Mar 19, 2024 · 3905b14 · 3905b14
1 parent 2c87492
commit 3905b14
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Showing 2 changed files with 16 additions and 15 deletions.
diff --git a/src/NeuralPDE.jl b/src/NeuralPDE.jl
@@ -24,14 +24,15 @@ using Symbolics: wrap, unwrap, arguments, operation
 using SymbolicUtils
 using AdvancedHMC, LogDensityProblems, LinearAlgebra, Functors, MCMCChains
 using MonteCarloMeasurements: Particles
-using ModelingToolkit: value, nameof, toexpr, build_expr, expand_derivatives, Interval, infimum, supremum
+using ModelingToolkit: value, nameof, toexpr, build_expr, expand_derivatives, Interval,
+ infimum, supremum
 import DomainSets
-using DomainSets: Domain, ClosedInterval, AbstractInterval, leftendpoint, rightendpoint, ProductDomain
+using DomainSets: Domain, ClosedInterval, AbstractInterval, leftendpoint, rightendpoint,
+ ProductDomain
 using SciMLBase: @add_kwonly, parameterless_type
 using UnPack: @unpack
 import ChainRulesCore, Lux, ComponentArrays
 using ChainRulesCore: @non_differentiable
-using NeuralOperators
 
 RuntimeGeneratedFunctions.init(@__MODULE__)
 
@@ -56,16 +57,16 @@ include("PDE_BPINN.jl")
 include("dgm.jl")
 
 export NNODE, NNDAE, PINOODE, TRAINSET
- PhysicsInformedNN, discretize,
- GridTraining, StochasticTraining, QuadratureTraining, QuasiRandomTraining,
- WeightedIntervalTraining,
- build_loss_function, get_loss_function,
- generate_training_sets, get_variables, get_argument, get_bounds,
- get_numeric_integral, symbolic_discretize,
- AbstractAdaptiveLoss, NonAdaptiveLoss, GradientScaleAdaptiveLoss,
- MiniMaxAdaptiveLoss, LogOptions,
- ahmc_bayesian_pinn_ode, BNNODE, ahmc_bayesian_pinn_pde, vector_to_parameters,
- BPINNsolution, BayesianPINN,
- DeepGalerkin
+  PhysicsInformedNN, discretize,
+  GridTraining, StochasticTraining, QuadratureTraining, QuasiRandomTraining,
+  WeightedIntervalTraining,
+  build_loss_function, get_loss_function,
+  generate_training_sets, get_variables, get_argument, get_bounds,
+  get_numeric_integral, symbolic_discretize,
+  AbstractAdaptiveLoss, NonAdaptiveLoss, GradientScaleAdaptiveLoss,
+  MiniMaxAdaptiveLoss, LogOptions,
+  ahmc_bayesian_pinn_ode, BNNODE, ahmc_bayesian_pinn_pde, vector_to_parameters,
+  BPINNsolution, BayesianPINN,
+  DeepGalerkin
 
 end # module
diff --git a/test/PINO_ode_tests.jl b/test/PINO_ode_tests.jl
@@ -81,7 +81,7 @@ begin
  * output data: set of solutions u(t){u0} corresponding initial conditions 'u0'.
  """
  train_set = TRAINSET(prob_set, u_output_; isu0 = true)
- #TODO we argument u0 but dont actualy use u0 because we use only set of u0 for generate train set from prob_set
+ #TODO we argument u0 but dont actually use u0 because we use only set of u0 for generate train set from prob_set
  prob = ODEProblem(linear, 0.0f0, tspan, p)
  fno = FourierNeuralOperator(ch = (2, 16, 16, 16, 16, 16, 32, 1), modes = (16,), σ = gelu)
  opt = OptimizationOptimisers.Adam(0.001)