From 3905b1433af1e193618f1e9d40ad473612e654ef Mon Sep 17 00:00:00 2001
From: KirillZubov <kirillzubov3@gmail.com>
Date: Tue, 19 Mar 2024 17:26:43 +0400
Subject: [PATCH] fix typo

---
 src/NeuralPDE.jl       | 29 +++++++++++++++--------------
 test/PINO_ode_tests.jl |  2 +-
 2 files changed, 16 insertions(+), 15 deletions(-)

diff --git a/src/NeuralPDE.jl b/src/NeuralPDE.jl
index 30e59486e7..68e7073ab4 100644
--- 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
index 0d35b5c166..e733b16a0c 100644
--- 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)