chore: fix typos

SciML · Jun 29, 2024 · 57709ad · 57709ad
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Showing 3 changed files with 5 additions and 5 deletions.
diff --git a/docs/src/examples/nonlinear_elliptic.md b/docs/src/examples/nonlinear_elliptic.md
@@ -89,15 +89,15 @@ sym_prob = NeuralPDE.symbolic_discretize(pdesystem, discretization)
 
 pde_inner_loss_functions = sym_prob.loss_functions.pde_loss_functions
 bcs_inner_loss_functions = sym_prob.loss_functions.bc_loss_functions[1:6]
-aprox_derivative_loss_functions = sym_prob.loss_functions.bc_loss_functions[7:end]
+approx_derivative_loss_functions = sym_prob.loss_functions.bc_loss_functions[7:end]
 
 global iteration = 0
 callback = function (p, l)
     if iteration % 10 == 0
         println("loss: ", l)
         println("pde_losses: ", map(l_ -> l_(p.u), pde_inner_loss_functions))
         println("bcs_losses: ", map(l_ -> l_(p.u), bcs_inner_loss_functions))
-        println("der_losses: ", map(l_ -> l_(p.u), aprox_derivative_loss_functions))
+        println("der_losses: ", map(l_ -> l_(p.u), approx_derivative_loss_functions))
     end
     global iteration += 1
     return false

diff --git a/docs/src/tutorials/constraints.md b/docs/src/tutorials/constraints.md
@@ -74,7 +74,7 @@ sym_prob = NeuralPDE.symbolic_discretize(pdesystem, discretization)
 
 pde_inner_loss_functions = sym_prob.loss_functions.pde_loss_functions
 bcs_inner_loss_functions = sym_prob.loss_functions.bc_loss_functions
-aprox_derivative_loss_functions = sym_prob.loss_functions.bc_loss_functions
+approx_derivative_loss_functions = sym_prob.loss_functions.bc_loss_functions
 
 cb_ = function (p, l)
     println("loss: ", l)

diff --git a/docs/src/tutorials/derivative_neural_network.md b/docs/src/tutorials/derivative_neural_network.md
@@ -102,13 +102,13 @@ sym_prob = NeuralPDE.symbolic_discretize(pdesystem, discretization)
 
 pde_inner_loss_functions = sym_prob.loss_functions.pde_loss_functions
 bcs_inner_loss_functions = sym_prob.loss_functions.bc_loss_functions[1:7]
-aprox_derivative_loss_functions = sym_prob.loss_functions.bc_loss_functions[9:end]
+approx_derivative_loss_functions = sym_prob.loss_functions.bc_loss_functions[9:end]
 
 callback = function (p, l)
     println("loss: ", l)
     println("pde_losses: ", map(l_ -> l_(p.u), pde_inner_loss_functions))
     println("bcs_losses: ", map(l_ -> l_(p.u), bcs_inner_loss_functions))
-    println("der_losses: ", map(l_ -> l_(p.u), aprox_derivative_loss_functions))
+    println("der_losses: ", map(l_ -> l_(p.u), approx_derivative_loss_functions))
     return false
 end