doc(conditions): minor typo fix
@@ -22,7 +22,7 @@ class BaseCondition:
self.ith_unit = None
def parameterize(self, output_tensor, *input_tensors):
- r"""Re-parameterize output(s) of a network.
+ r"""Re-parameterizes output(s) of a network.
:param output_tensor: Output of the neural network.
:type output_tensor: `torch.Tensor`
@@ -155,7 +155,7 @@ class IVP(BaseCondition):
self.t_0, self.x_0, self.x_0_prime = t_0, x_0, x_0_prime
def parameterize(self, output_tensor, t):
- r"""Re-parameterize outputs such that the Dirichlet/Neumann condition is satisfied.
+ r"""Re-parameterizes outputs such that the Dirichlet/Neumann condition is satisfied.
- For Dirichlet condition, the re-parameterization is
:math:`\displaystyle x(t) = x_0 + \left(1 - e^{-(t-t_0)}\right) \mathrm{ANN}(t)`
@@ -196,7 +196,7 @@ class DirichletBVP(BaseCondition):
self.t_0, self.x_0, self.t_1, self.x_1 = t_0, x_0, t_1, x_1
def parameterize(self, output_tensor, t):
- r"""Re-parameterize outputs such that the Dirichlet condition is satisfied on both ends of the domain.
+ r"""Re-parameterizes outputs such that the Dirichlet condition is satisfied on both ends of the domain.
The re-parameterization is
:math:`\displaystyle x(t)=(1-\tilde{t})x_0+\tilde{t}x_1+\left(1-e^{(1-\tilde{t})\tilde{t}}\right)\mathrm{ANN}(t)`,
@@ -252,7 +252,7 @@ class DirichletBVP2D(BaseCondition):
self.y1, self.g1 = y_max, y_max_val
def parameterize(self, output_tensor, x, y):
- r"""Re-parameterize outputs such that the Dirichlet condition is satisfied on all four sides of the domain.
+ r"""Re-parameterizes outputs such that the Dirichlet condition is satisfied on all four sides of the domain.
The re-parameterization is
:math:`\displaystyle u(x,y)=A(x,y)
