diff --git a/docs/source/_rst/_code.rst b/docs/source/_rst/_code.rst
index 25f0e3062..93787a0b2 100644
--- a/docs/source/_rst/_code.rst
+++ b/docs/source/_rst/_code.rst
@@ -95,6 +95,7 @@ Models
     MultiFeedForward <model/multi_feed_forward.rst>
     ResidualFeedForward <model/residual_feed_forward.rst>
     Spline <model/spline.rst>
+    SplineSurface <model/spline_surface.rst>
     DeepONet <model/deeponet.rst>
     MIONet <model/mionet.rst>
     KernelNeuralOperator <model/kernel_neural_operator.rst>
diff --git a/docs/source/_rst/model/spline_surface.rst b/docs/source/_rst/model/spline_surface.rst
new file mode 100644
index 000000000..6bbf137d8
--- /dev/null
+++ b/docs/source/_rst/model/spline_surface.rst
@@ -0,0 +1,7 @@
+Spline Surface
+================
+.. currentmodule:: pina.model.spline_surface
+
+.. autoclass:: SplineSurface
+    :members:
+    :show-inheritance:
\ No newline at end of file
diff --git a/pina/model/__init__.py b/pina/model/__init__.py
index ee343e53d..b67e3562b 100644
--- a/pina/model/__init__.py
+++ b/pina/model/__init__.py
@@ -25,6 +25,7 @@
 from .average_neural_operator import AveragingNeuralOperator
 from .low_rank_neural_operator import LowRankNeuralOperator
 from .spline import Spline
+from .spline_surface import SplineSurface
 from .graph_neural_operator import GraphNeuralOperator
 from .pirate_network import PirateNet
 from .equivariant_graph_neural_operator import EquivariantGraphNeuralOperator
diff --git a/pina/model/spline.py b/pina/model/spline.py
index c22c7937c..fd6df3fe7 100644
--- a/pina/model/spline.py
+++ b/pina/model/spline.py
@@ -1,109 +1,238 @@
-"""Module for the Spline model class."""
+"""Module for the B-Spline model class."""
 
+import warnings
 import torch
-from ..utils import check_consistency
+from ..utils import check_positive_integer
 
 
 class Spline(torch.nn.Module):
-    """
-    Spline model class.
+    r"""
+    The univariate B-Spline curve model class.
+
+    A univariate B-spline curve of order :math:`k` is a parametric curve defined
+    as a linear combination of B-spline basis functions and control points:
+
+    .. math::
+
+        S(x) = \sum_{i=1}^{n} B_{i,k}(x) C_i, \quad x \in [x_1, x_m]
+
+    where:
+
+    - :math:`C_i \in \mathbb{R}` are the control points. These fixed points
+      influence the shape of the curve but are not generally interpolated,
+      except at the boundaries under certain knot multiplicities.
+    - :math:`B_{i,k}(x)` are the B-spline basis functions of order :math:`k`,
+      i.e., piecewise polynomials of degree :math:`k-1` with support on the
+      interval :math:`[x_i, x_{i+k}]`.
+    - :math:`X = \{ x_1, x_2, \dots, x_m \}` is the non-decreasing knot vector.
+
+    If the first and last knots are repeated :math:`k` times, then the curve
+    interpolates the first and last control points.
+
+
+    .. note::
+
+        The curve is forced to be zero outside the interval defined by the
+        first and last knots.
+
+
+    :Example:
+
+    >>> from pina.model import Spline
+    >>> import torch
+
+    >>> knots1 = torch.tensor([0.0, 0.0, 0.0, 1.0, 2.0, 2.0, 2.0])
+    >>> spline1 = Spline(order=3, knots=knots1, control_points=None)
+
+    >>> knots2 = {"n": 7, "min": 0.0, "max": 2.0, "mode": "auto"}
+    >>> spline2 = Spline(order=3, knots=knots2, control_points=None)
+
+    >>> knots3 = torch.tensor([0.0, 0.0, 0.0, 1.0, 2.0, 2.0, 2.0])
+    >>> control_points3 = torch.tensor([0.0, 1.0, 3.0, 2.0])
+    >>> spline3 = Spline(order=3, knots=knots3, control_points=control_points3)
     """
 
-    def __init__(self, order=4, knots=None, control_points=None) -> None:
+    def __init__(self, order=4, knots=None, control_points=None):
         """
         Initialization of the :class:`Spline` class.
 
-        :param int order: The order of the spline. Default is ``4``.
-        :param torch.Tensor knots: The tensor representing knots. If ``None``,
-            the knots will be initialized automatically. Default is ``None``.
-        :param torch.Tensor control_points: The control points. Default is
-            ``None``.
-        :raises ValueError: If the order is negative.
-        :raises ValueError: If both knots and control points are ``None``.
-        :raises ValueError: If the knot tensor is not one-dimensional.
+        :param int order: The order of the spline. The corresponding basis
+            functions are polynomials of degree ``order - 1``. Default is 4.
+        :param knots: The knots of the spline. If a tensor is provided, knots
+            are set directly from the tensor. If a dictionary is provided, it
+            must contain the keys ``"n"``, ``"min"``, ``"max"``, and ``"mode"``.
+            Here, ``"n"`` specifies the number of knots, ``"min"`` and ``"max"``
+            define the interval, and ``"mode"`` selects the sampling strategy.
+            The supported modes are ``"uniform"``, where the knots are evenly
+            spaced over :math:`[min, max]`, and ``"auto"``, where knots are
+            constructed to ensure that the spline interpolates the first and
+            last control points. In this case, the number of knots is adjusted
+            if :math:`n < 2 * order`. If None is given, knots are initialized
+            automatically over :math:`[0, 1]` ensuring interpolation of the
+            first and last control points. Default is None.
+        :type knots: torch.Tensor | dict
+        :param torch.Tensor control_points: The control points of the spline.
+            If None, they are initialized as learnable parameters with an
+            initial value of zero. Default is None.
+        :raises AssertionError: If ``order`` is not a positive integer.
+        :raises ValueError: If both ``knots`` and ``control_points`` are None.
+        :raises ValueError: If ``knots`` is not one-dimensional.
+        :raises ValueError: If ``control_points`` is not one-dimensional.
+        :raises ValueError: If the number of ``knots`` is not equal to the sum
+            of ``order`` and the number of ``control_points.``
+        :raises UserWarning: If the number of control points is lower than the
+            order, resulting in a degenerate spline.
         """
         super().__init__()
 
-        check_consistency(order, int)
+        # Check consistency
+        check_positive_integer(value=order, strict=True)
 
-        if order < 0:
-            raise ValueError("Spline order cannot be negative.")
+        # Raise error if neither knots nor control points are provided
         if knots is None and control_points is None:
-            raise ValueError("Knots and control points cannot be both None.")
+            raise ValueError("knots and control_points cannot both be None.")
 
-        self.order = order
-        self.k = order - 1
-
-        if knots is not None and control_points is not None:
-            self.knots = knots
-            self.control_points = control_points
+        # Initialize knots if not provided
+        if knots is None and control_points is not None:
+            knots = {
+                "n": len(control_points) + order,
+                "min": 0,
+                "max": 1,
+                "mode": "auto",
+            }
 
-        elif knots is not None:
-            print("Warning: control points will be initialized automatically.")
-            print("         experimental feature")
+        # Initialization - knots and control points managed by their setters
+        self.order = order
+        self.knots = knots
+        self.control_points = control_points
+
+        # Check dimensionality of knots
+        if self.knots.ndim > 1:
+            raise ValueError("knots must be one-dimensional.")
+
+        # Check dimensionality of control points
+        if self.control_points.ndim > 1:
+            raise ValueError("control_points must be one-dimensional.")
+
+        # Raise error if #knots != order + #control_points
+        if len(self.knots) != self.order + len(self.control_points):
+            raise ValueError(
+                f" The number of knots must be equal to order + number of"
+                f" control points. Got {len(self.knots)} knots, {self.order}"
+                f" order and {len(self.control_points)} control points."
+            )
 
-            self.knots = knots
-            n = len(knots) - order
-            self.control_points = torch.nn.Parameter(
-                torch.zeros(n), requires_grad=True
+        # Raise warning if spline is degenerate
+        if len(self.control_points) < self.order:
+            warnings.warn(
+                "The number of control points is smaller than the spline order."
+                " This creates a degenerate spline with limited flexibility.",
+                UserWarning,
             )
 
-        elif control_points is not None:
-            print("Warning: knots will be initialized automatically.")
-            print("         experimental feature")
+        # Precompute boundary interval index
+        self._boundary_interval_idx = self._compute_boundary_interval()
 
-            self.control_points = control_points
+    def _compute_boundary_interval(self):
+        """
+        Precompute the index of the rightmost non-degenerate interval to improve
+        performance, eliminating the need to perform a search loop in the basis
+        function on each call.
 
-            n = len(self.control_points) - 1
-            self.knots = {
-                "type": "auto",
-                "min": 0,
-                "max": 1,
-                "n": n + 2 + self.order,
-            }
+        :return: The index of the rightmost non-degenerate interval.
+        :rtype: int
+        """
+        # Return 0 if there is a single interval
+        if len(self.knots) < 2:
+            return 0
 
-        else:
-            raise ValueError("Knots and control points cannot be both None.")
+        # Find all indices where knots are strictly increasing
+        diffs = self.knots[1:] - self.knots[:-1]
+        valid = torch.nonzero(diffs > 0, as_tuple=False)
 
-        if self.knots.ndim != 1:
-            raise ValueError("Knot vector must be one-dimensional.")
+        # If all knots are equal, return 0 for degenerate spline
+        if valid.numel() == 0:
+            return 0
 
-    def basis(self, x, k, i, t):
+        # Otherwise, return the last valid index
+        return int(valid[-1])
+
+    def basis(self, x):
         """
-        Recursive method to compute the basis functions of the spline.
+        Compute the basis functions for the spline using an iterative approach.
+        This is a vectorized implementation based on the Cox-de Boor recursion.
 
         :param torch.Tensor x: The points to be evaluated.
-        :param int k: The spline degree.
-        :param int i: The index of the interval.
-        :param torch.Tensor t: The tensor of knots.
-        :return: The basis functions evaluated at x
+        :return: The basis functions evaluated at x.
         :rtype: torch.Tensor
         """
+        # Add a final dimension to x
+        x = x.unsqueeze(-1)
+
+        # Add an initial dimension to knots
+        knots = self.knots.unsqueeze(0)
+
+        # Base case of recursion: indicator functions for the intervals
+        basis = (x >= knots[..., :-1]) & (x < knots[..., 1:])
+        basis = basis.to(x.dtype)
 
-        if k == 0:
-            a = torch.where(
-                torch.logical_and(t[i] <= x, x < t[i + 1]), 1.0, 0.0
+        # One-dimensional knots case: ensure rightmost boundary inclusion
+        if self._boundary_interval_idx is not None:
+
+            # Extract left and right knots of the rightmost interval
+            knot_left = knots[..., self._boundary_interval_idx]
+            knot_right = knots[..., self._boundary_interval_idx + 1]
+
+            # Identify points at the rightmost boundary
+            at_rightmost_boundary = (
+                x.squeeze(-1) >= knot_left
+            ) & torch.isclose(x.squeeze(-1), knot_right, rtol=1e-8, atol=1e-10)
+
+            # Ensure the correct value is set at the rightmost boundary
+            if torch.any(at_rightmost_boundary):
+                basis[..., self._boundary_interval_idx] = torch.logical_or(
+                    basis[..., self._boundary_interval_idx].bool(),
+                    at_rightmost_boundary,
+                ).to(basis.dtype)
+
+        # Iterative case of recursion
+        for i in range(1, self.order):
+
+            # Compute the denominators for both terms
+            denom1 = knots[..., i:-1] - knots[..., : -(i + 1)]
+            denom2 = knots[..., i + 1 :] - knots[..., 1:-i]
+
+            # Ensure no division by zero
+            denom1 = torch.where(
+                torch.abs(denom1) < 1e-8, torch.ones_like(denom1), denom1
             )
-            if i == len(t) - self.order - 1:
-                a = torch.where(x == t[-1], 1.0, a)
-            a.requires_grad_(True)
-            return a
-
-        if t[i + k] == t[i]:
-            c1 = torch.tensor([0.0] * len(x), requires_grad=True)
-        else:
-            c1 = (x - t[i]) / (t[i + k] - t[i]) * self.basis(x, k - 1, i, t)
-
-        if t[i + k + 1] == t[i + 1]:
-            c2 = torch.tensor([0.0] * len(x), requires_grad=True)
-        else:
-            c2 = (
-                (t[i + k + 1] - x)
-                / (t[i + k + 1] - t[i + 1])
-                * self.basis(x, k - 1, i + 1, t)
+            denom2 = torch.where(
+                torch.abs(denom2) < 1e-8, torch.ones_like(denom2), denom2
             )
 
-        return c1 + c2
+            # Compute the two terms of the recursion
+            term1 = ((x - knots[..., : -(i + 1)]) / denom1) * basis[..., :-1]
+            term2 = ((knots[..., i + 1 :] - x) / denom2) * basis[..., 1:]
+
+            # Combine terms to get the new basis
+            basis = term1 + term2
+
+        return basis
+
+    def forward(self, x):
+        """
+        Forward pass for the :class:`Spline` model.
+
+        :param x: The input tensor.
+        :type x: torch.Tensor | LabelTensor
+        :return: The output tensor.
+        :rtype: torch.Tensor
+        """
+        return torch.einsum(
+            "bi, i -> b",
+            self.basis(x.as_subclass(torch.Tensor)).squeeze(1),
+            self.control_points,
+        ).reshape(-1, 1)
 
     @property
     def control_points(self):
@@ -116,24 +245,42 @@ def control_points(self):
         return self._control_points
 
     @control_points.setter
-    def control_points(self, value):
+    def control_points(self, control_points):
         """
         Set the control points of the spline.
 
-        :param value: The control points.
-        :type value: torch.Tensor | dict
-        :raises ValueError: If invalid value is passed.
+        :param torch.Tensor control_points: The control points tensor. If None,
+            control points are initialized to learnable parameters with zero
+            initial value. Default is None.
+        :raises ValueError: If there are not enough knots to define the control
+            points, due to the relation: #knots = order + #control_points.
+        :raises ValueError: If control_points is not a torch.Tensor.
         """
-        if isinstance(value, dict):
-            if "n" not in value:
-                raise ValueError("Invalid value for control_points")
-            n = value["n"]
-            dim = value.get("dim", 1)
-            value = torch.zeros(n, dim)
+        # If control points are not provided, initialize them
+        if control_points is None:
+
+            # Check that there are enough knots to define control points
+            if len(self.knots) < self.order + 1:
+                raise ValueError(
+                    f"Not enough knots to define control points. Got "
+                    f"{len(self.knots)} knots, but need at least "
+                    f"{self.order + 1}."
+                )
+
+            # Initialize control points to zero
+            control_points = torch.zeros(len(self.knots) - self.order)
+
+        # Check validity of control points
+        elif not isinstance(control_points, torch.Tensor):
+            raise ValueError(
+                "control_points must be a torch.Tensor,"
+                f" got {type(control_points)}"
+            )
 
-        if not isinstance(value, torch.Tensor):
-            raise ValueError("Invalid value for control_points")
-        self._control_points = torch.nn.Parameter(value, requires_grad=True)
+        # Set control points
+        self._control_points = torch.nn.Parameter(
+            control_points, requires_grad=True
+        )
 
     @property
     def knots(self):
@@ -150,50 +297,80 @@ def knots(self, value):
         """
         Set the knots of the spline.
 
-        :param value: The knots.
+        :param value: The knots of the spline. If a tensor is provided, knots
+            are set directly from the tensor. If a dictionary is provided, it
+            must contain the keys ``"n"``, ``"min"``, ``"max"``, and ``"mode"``.
+            Here, ``"n"`` specifies the number of knots, ``"min"`` and ``"max"``
+            define the interval, and ``"mode"`` selects the sampling strategy.
+            The supported modes are ``"uniform"``, where the knots are evenly
+            spaced over :math:`[min, max]`, and ``"auto"``, where knots are
+            constructed to ensure that the spline interpolates the first and
+            last control points. In this case, the number of knots is inferred
+            and the ``"n"`` key is ignored.
         :type value: torch.Tensor | dict
-        :raises ValueError: If invalid value is passed.
+        :raises ValueError: If value is not a torch.Tensor or a dictionary.
+        :raises ValueError: If a dictionary is provided but does not contain
+            the required keys.
+        :raises ValueError: If the mode specified in the dictionary is invalid.
         """
+        # Check validity of knots
+        if not isinstance(value, (torch.Tensor, dict)):
+            raise ValueError(
+                "Knots must be a torch.Tensor or a dictionary,"
+                f" got {type(value)}."
+            )
+
+        # If a dictionary is provided, initialize knots accordingly
         if isinstance(value, dict):
 
-            type_ = value.get("type", "auto")
-            min_ = value.get("min", 0)
-            max_ = value.get("max", 1)
-            n = value.get("n", 10)
-
-            if type_ == "uniform":
-                value = torch.linspace(min_, max_, n + self.k + 1)
-            elif type_ == "auto":
-                initial_knots = torch.ones(self.order + 1) * min_
-                final_knots = torch.ones(self.order + 1) * max_
-
-                if n < self.order + 1:
-                    value = torch.concatenate((initial_knots, final_knots))
-                elif n - 2 * self.order + 1 == 1:
-                    value = torch.Tensor([(max_ + min_) / 2])
-                else:
-                    value = torch.linspace(min_, max_, n - 2 * self.order - 1)
+            # Check that required keys are present
+            required_keys = {"n", "min", "max", "mode"}
+            if not required_keys.issubset(value.keys()):
+                raise ValueError(
+                    f"When providing knots as a dictionary, the following "
+                    f"keys must be present: {required_keys}. Got "
+                    f"{value.keys()}."
+                )
 
-                value = torch.concatenate((initial_knots, value, final_knots))
+            # Uniform sampling of knots
+            if value["mode"] == "uniform":
+                value = torch.linspace(value["min"], value["max"], value["n"])
 
-        if not isinstance(value, torch.Tensor):
-            raise ValueError("Invalid value for knots")
+            # Automatic sampling of interpolating knots
+            elif value["mode"] == "auto":
 
-        self._knots = value
+                # Repeat the first and last knots 'order' times
+                initial_knots = torch.ones(self.order) * value["min"]
+                final_knots = torch.ones(self.order) * value["max"]
 
-    def forward(self, x):
-        """
-        Forward pass for the :class:`Spline` model.
+                # Number of internal knots
+                n_internal = value["n"] - 2 * self.order
 
-        :param torch.Tensor x: The input tensor.
-        :return: The output tensor.
-        :rtype: torch.Tensor
-        """
-        t = self.knots
-        k = self.k
-        c = self.control_points
-
-        basis = map(lambda i: self.basis(x, k, i, t)[:, None], range(len(c)))
-        y = (torch.cat(list(basis), dim=1) * c).sum(axis=1)
+                # If no internal knots are needed, just concatenate boundaries
+                if n_internal <= 0:
+                    value = torch.cat((initial_knots, final_knots))
 
-        return y
+                # Else, sample internal knots uniformly and exclude boundaries
+                # Recover the correct number of internal knots when slicing by
+                # adding 2 to n_internal
+                else:
+                    internal_knots = torch.linspace(
+                        value["min"], value["max"], n_internal + 2
+                    )[1:-1]
+                    value = torch.cat(
+                        (initial_knots, internal_knots, final_knots)
+                    )
+
+            # Raise error if mode is invalid
+            else:
+                raise ValueError(
+                    f"Invalid mode for knots initialization. Got "
+                    f"{value['mode']}, but expected 'uniform' or 'auto'."
+                )
+
+        # Set knots
+        self.register_buffer("_knots", value.sort(dim=0).values)
+
+        # Recompute boundary interval when knots change
+        if hasattr(self, "_boundary_interval_idx"):
+            self._boundary_interval_idx = self._compute_boundary_interval()
diff --git a/pina/model/spline_surface.py b/pina/model/spline_surface.py
new file mode 100644
index 000000000..288b77a72
--- /dev/null
+++ b/pina/model/spline_surface.py
@@ -0,0 +1,140 @@
+"""Module for the bivariate B-Spline surface model class."""
+
+import torch
+from .spline import Spline
+from ..utils import check_consistency
+
+
+class SplineSurface(torch.nn.Module):
+    r"""
+    The bivariate B-Spline surface model class.
+
+    A bivariate B-spline surface is a parametric surface defined as the tensor
+    product of two univariate B-spline curves:
+
+    .. math::
+
+        S(x, y) = \sum_{i,j=1}^{n_x, n_y} B_{i,k}(x) B_{j,s}(y) C_{i,j},
+        \quad x \in [x_1, x_m], y \in [y_1, y_l]
+
+    where:
+
+    - :math:`C_{i,j} \in \mathbb{R}^2` are the control points. These fixed
+      points influence the shape of the surface but are not generally
+      interpolated, except at the boundaries under certain knot multiplicities.
+    - :math:`B_{i,k}(x)` and :math:`B_{j,s}(y)` are the B-spline basis functions
+      defined over two orthogonal directions, with orders :math:`k` and
+      :math:`s`, respectively.
+    - :math:`X = \{ x_1, x_2, \dots, x_m \}` and
+      :math:`Y = \{ y_1, y_2, \dots, y_l \}` are the non-decreasing knot
+      vectors along the two directions.
+    """
+
+    def __init__(self, orders, knots_u, knots_v, control_points=None):
+        """
+        Initialization of the :class:`SplineSurface` class.
+
+        :param list[int] orders: The orders of the spline along each parametric
+            direction. Each order defines the degree of the corresponding basis
+            as ``degree = order - 1``.
+        :param knots_u: The knots of the spline along the first direction.
+            Unlike the univariate case, this must be explicitly provided.
+            For details on valid formats and initialization modes, see the
+            :class:`Spline` class.
+        :type knots_u: torch.Tensor | dict
+        :param knots_v: The knots of the spline along the second direction.
+            Unlike the univariate case, this must be explicitly provided.
+            For details on valid formats and initialization modes, see the
+            :class:`Spline` class.
+        :type knots_v: torch.Tensor | dict
+        :param torch.Tensor control_points: The control points defining the
+            surface geometry. It must be a two-dimensional tensor of shape
+            ``[len(knots_u) - orders[0], len(knots_v) - orders[1]]``.
+            If None, they are initialized as learnable parameters with zero
+            values. Default is None.
+        :raises ValueError: If ``orders`` is not a list of two elements.
+        :raises ValueError: If ``knots_u`` or ``knots_v`` is None.
+        :raises ValueError: If ``control_points`` is not a torch.Tensor when
+            provided.
+        :raises ValueError: If ``control_points`` is not of the correct shape
+            when provided.
+        """
+        super().__init__()
+
+        # Check consistency
+        check_consistency(orders, int)
+        check_consistency(control_points, (type(None), torch.Tensor))
+
+        # Check orders is a list of two elements
+        if len(orders) != 2:
+            raise ValueError("orders must be a list of two elements.")
+
+        # Check knots_u and knots_v are not None
+        if knots_u is None or knots_v is None:
+            raise ValueError("knots_u and knots_v must cannot be None.")
+
+        # Create two univariate b-splines
+        self.spline_u = Spline(order=orders[0], knots=knots_u)
+        self.spline_v = Spline(order=orders[1], knots=knots_v)
+
+        # Delete unneeded parameters
+        delattr(self.spline_u, "_control_points")
+        delattr(self.spline_v, "_control_points")
+
+        # Save correct shape of control points
+        __valid_shape = (
+            len(self.spline_u.knots) - self.spline_u.order,
+            len(self.spline_v.knots) - self.spline_v.order,
+        )
+
+        # Initialize control points, if not provided
+        if control_points is None:
+            control_points = torch.zeros(__valid_shape)
+
+        # Check control points
+        if control_points.shape != __valid_shape:
+            raise ValueError(
+                "control_points must be of the correct shape. ",
+                f"Expected {__valid_shape}, got {control_points.shape}.",
+            )
+
+        # Register control points as a learnable parameter
+        self._control_points = torch.nn.Parameter(
+            control_points, requires_grad=True
+        )
+
+    def forward(self, x):
+        """
+        Forward pass for the :class:`SplineSurface` model.
+
+        :param x: The input tensor.
+        :type x: torch.Tensor | LabelTensor
+        :return: The output tensor.
+        :rtype: torch.Tensor
+        """
+        return torch.einsum(
+            "bi, bj, ij -> b",
+            self.spline_u.basis(x.as_subclass(torch.Tensor)[:, 0]),
+            self.spline_v.basis(x.as_subclass(torch.Tensor)[:, 1]),
+            self.control_points,
+        ).reshape(-1, 1)
+
+    @property
+    def knots(self):
+        """
+        The knots of the univariate splines defining the spline surface.
+
+        :return: The knots.
+        :rtype: tuple(torch.Tensor, torch.Tensor)
+        """
+        return self.spline_u.knots, self.spline_v.knots
+
+    @property
+    def control_points(self):
+        """
+        The control points of the spline.
+
+        :return: The control points.
+        :rtype: torch.Tensor
+        """
+        return self._control_points
diff --git a/tests/test_model/test_spline.py b/tests/test_model/test_spline.py
index d38b1610b..c30e54244 100644
--- a/tests/test_model/test_spline.py
+++ b/tests/test_model/test_spline.py
@@ -1,81 +1,171 @@
 import torch
 import pytest
-
+import numpy as np
+from scipy.interpolate import BSpline
 from pina.model import Spline
+from pina import LabelTensor
 
-data = torch.rand((20, 3))
-input_vars = 3
-output_vars = 4
 
-valid_args = [
-    {
-        "knots": torch.tensor([0.0, 0.0, 0.0, 1.0, 2.0, 3.0, 3.0, 3.0]),
-        "control_points": torch.tensor([0.0, 0.0, 1.0, 0.0, 0.0]),
-        "order": 3,
-    },
-    {
-        "knots": torch.tensor(
-            [-2.0, -2.0, -2.0, -2.0, -1.0, 0.0, 1.0, 2.0, 2.0, 2.0, 2.0]
-        ),
-        "control_points": torch.tensor([0.0, 0.0, 0.0, 6.0, 0.0, 0.0, 0.0]),
-        "order": 4,
-    },
-    # {'control_points': {'n': 5, 'dim': 1}, 'order': 2},
-    # {'control_points': {'n': 7, 'dim': 1}, 'order': 3}
-]
+# Utility quantities for testing
+order = torch.randint(1, 8, (1,)).item()
+n_ctrl_pts = torch.randint(order, order + 5, (1,)).item()
+n_knots = order + n_ctrl_pts
+
+# Input tensor
+pts = LabelTensor(torch.linspace(0, 1, 100).reshape(-1, 1), ["x"])
 
 
-def scipy_check(model, x, y):
-    from scipy.interpolate._bsplines import BSpline
-    import numpy as np
+# Function to compare with scipy implementation
+def check_scipy_spline(model, x, output_):
 
-    spline = BSpline(
+    # Define scipy spline
+    scipy_spline = BSpline(
         t=model.knots.detach().numpy(),
         c=model.control_points.detach().numpy(),
         k=model.order - 1,
     )
-    y_scipy = spline(x).flatten()
-    y = y.detach().numpy()
-    np.testing.assert_allclose(y, y_scipy, atol=1e-5)
+
+    # Compare outputs
+    np.testing.assert_allclose(
+        output_.squeeze().detach().numpy(),
+        scipy_spline(x).flatten(),
+        atol=1e-5,
+        rtol=1e-5,
+    )
+
+
+# Define all possible combinations of valid arguments for the Spline class
+valid_args = [
+    {
+        "order": order,
+        "control_points": torch.rand(n_ctrl_pts),
+        "knots": torch.linspace(0, 1, n_knots),
+    },
+    {
+        "order": order,
+        "control_points": torch.rand(n_ctrl_pts),
+        "knots": {"n": n_knots, "min": 0, "max": 1, "mode": "auto"},
+    },
+    {
+        "order": order,
+        "control_points": torch.rand(n_ctrl_pts),
+        "knots": {"n": n_knots, "min": 0, "max": 1, "mode": "uniform"},
+    },
+    {
+        "order": order,
+        "control_points": None,
+        "knots": torch.linspace(0, 1, n_knots),
+    },
+    {
+        "order": order,
+        "control_points": None,
+        "knots": {"n": n_knots, "min": 0, "max": 1, "mode": "auto"},
+    },
+    {
+        "order": order,
+        "control_points": None,
+        "knots": {"n": n_knots, "min": 0, "max": 1, "mode": "uniform"},
+    },
+    {
+        "order": order,
+        "control_points": torch.rand(n_ctrl_pts),
+        "knots": None,
+    },
+]
 
 
 @pytest.mark.parametrize("args", valid_args)
 def test_constructor(args):
     Spline(**args)
 
+    # Should fail if order is not a positive integer
+    with pytest.raises(AssertionError):
+        Spline(
+            order=-1, control_points=args["control_points"], knots=args["knots"]
+        )
+
+    # Should fail if control_points is not None or a torch.Tensor
+    with pytest.raises(ValueError):
+        Spline(
+            order=args["order"], control_points=[1, 2, 3], knots=args["knots"]
+        )
+
+    # Should fail if knots is not None, a torch.Tensor, or a dict
+    with pytest.raises(ValueError):
+        Spline(
+            order=args["order"], control_points=args["control_points"], knots=5
+        )
 
-def test_constructor_wrong():
+    # Should fail if both knots and control_points are None
     with pytest.raises(ValueError):
-        Spline()
+        Spline(order=args["order"], control_points=None, knots=None)
+
+    # Should fail if knots is not one-dimensional
+    with pytest.raises(ValueError):
+        Spline(
+            order=args["order"],
+            control_points=args["control_points"],
+            knots=torch.rand(n_knots, 4),
+        )
+
+    # Should fail if control_points is not one-dimensional
+    with pytest.raises(ValueError):
+        Spline(
+            order=args["order"],
+            control_points=torch.rand(n_ctrl_pts, 4),
+            knots=args["knots"],
+        )
+
+    # Should fail if the number of knots != order + number of control points
+    # If control points are None, they are initialized to fulfill this condition
+    if args["control_points"] is not None:
+        with pytest.raises(ValueError):
+            Spline(
+                order=args["order"],
+                control_points=args["control_points"],
+                knots=torch.linspace(0, 1, n_knots + 1),
+            )
+
+    # Should fail if the knot dict is missing required keys
+    with pytest.raises(ValueError):
+        Spline(
+            order=args["order"],
+            control_points=args["control_points"],
+            knots={"n": n_knots, "min": 0, "max": 1},
+        )
+
+    # Should fail if the knot dict has invalid 'mode' key
+    with pytest.raises(ValueError):
+        Spline(
+            order=args["order"],
+            control_points=args["control_points"],
+            knots={"n": n_knots, "min": 0, "max": 1, "mode": "invalid"},
+        )
 
 
 @pytest.mark.parametrize("args", valid_args)
 def test_forward(args):
-    min_x = args["knots"][0]
-    max_x = args["knots"][-1]
-    xi = torch.linspace(min_x, max_x, 1000)
+
+    # Define the model
     model = Spline(**args)
-    yi = model(xi).squeeze()
-    scipy_check(model, xi, yi)
-    return
+
+    # Evaluate the model
+    output_ = model(pts)
+    assert output_.shape == (pts.shape[0], 1)
+
+    # Compare with scipy implementation only for interpolant knots (mode: auto)
+    if isinstance(args["knots"], dict) and args["knots"]["mode"] == "auto":
+        check_scipy_spline(model, pts, output_)
 
 
 @pytest.mark.parametrize("args", valid_args)
 def test_backward(args):
-    min_x = args["knots"][0]
-    max_x = args["knots"][-1]
-    xi = torch.linspace(min_x, max_x, 100)
+
+    # Define the model
     model = Spline(**args)
-    yi = model(xi)
-    fake_loss = torch.sum(yi)
-    assert model.control_points.grad is None
-    fake_loss.backward()
-    assert model.control_points.grad is not None
-
-    # dim_in, dim_out = 3, 2
-    # fnn = FeedForward(dim_in, dim_out)
-    # data.requires_grad = True
-    # output_ = fnn(data)
-    # l=torch.mean(output_)
-    # l.backward()
-    # assert data._grad.shape == torch.Size([20,3])
+
+    # Evaluate the model
+    output_ = model(pts)
+    loss = torch.mean(output_)
+    loss.backward()
+    assert model.control_points.grad.shape == model.control_points.shape
diff --git a/tests/test_model/test_spline_surface.py b/tests/test_model/test_spline_surface.py
new file mode 100644
index 000000000..6fc0583e3
--- /dev/null
+++ b/tests/test_model/test_spline_surface.py
@@ -0,0 +1,159 @@
+import torch
+import random
+import pytest
+from pina.model import SplineSurface
+from pina import LabelTensor
+
+
+# Utility quantities for testing
+orders = [random.randint(1, 8) for _ in range(2)]
+n_ctrl_pts = random.randint(max(orders), max(orders) + 5)
+n_knots = [orders[i] + n_ctrl_pts for i in range(2)]
+
+# Input tensor
+x = torch.linspace(0, 1, 100).reshape(-1, 1)
+y = torch.linspace(0, 1, 100).reshape(-1, 1)
+pts = LabelTensor(torch.cat((x, y), dim=1), labels=["x", "y"])
+
+
+@pytest.mark.parametrize(
+    "knots_u",
+    [
+        torch.rand(n_knots[0]),
+        {"n": n_knots[0], "min": 0, "max": 1, "mode": "auto"},
+        {"n": n_knots[0], "min": 0, "max": 1, "mode": "uniform"},
+    ],
+)
+@pytest.mark.parametrize(
+    "knots_v",
+    [
+        torch.rand(n_knots[1]),
+        {"n": n_knots[1], "min": 0, "max": 1, "mode": "auto"},
+        {"n": n_knots[1], "min": 0, "max": 1, "mode": "uniform"},
+    ],
+)
+@pytest.mark.parametrize(
+    "control_points", [torch.rand(n_ctrl_pts, n_ctrl_pts), None]
+)
+def test_constructor(knots_u, knots_v, control_points):
+    SplineSurface(
+        orders=orders,
+        knots_u=knots_u,
+        knots_v=knots_v,
+        control_points=control_points,
+    )
+
+    # Should fail if orders is not list of two elements
+    with pytest.raises(ValueError):
+        SplineSurface(
+            orders=[orders[0]],
+            knots_u=knots_u,
+            knots_v=knots_v,
+            control_points=control_points,
+        )
+
+    # Should fail if knots_u is None
+    with pytest.raises(ValueError):
+        SplineSurface(
+            orders=orders,
+            knots_u=None,
+            knots_v=knots_v,
+            control_points=control_points,
+        )
+
+    # Should fail if knots_v is None
+    with pytest.raises(ValueError):
+        SplineSurface(
+            orders=orders,
+            knots_u=knots_u,
+            knots_v=None,
+            control_points=control_points,
+        )
+
+    # Should fail if control_points is not a torch.Tensor when provided
+    with pytest.raises(ValueError):
+        SplineSurface(
+            orders=orders,
+            knots_u=knots_u,
+            knots_v=knots_v,
+            control_points=[[0.0] * n_ctrl_pts] * n_ctrl_pts,
+        )
+
+    # Should fail if control_points is not of the correct shape when provided
+    with pytest.raises(ValueError):
+        SplineSurface(
+            orders=orders,
+            knots_u=knots_u,
+            knots_v=knots_v,
+            control_points=torch.rand(n_ctrl_pts + 1, n_ctrl_pts),
+        )
+
+
+@pytest.mark.parametrize(
+    "knots_u",
+    [
+        torch.rand(n_knots[0]),
+        {"n": n_knots[0], "min": 0, "max": 1, "mode": "auto"},
+        {"n": n_knots[0], "min": 0, "max": 1, "mode": "uniform"},
+    ],
+)
+@pytest.mark.parametrize(
+    "knots_v",
+    [
+        torch.rand(n_knots[1]),
+        {"n": n_knots[1], "min": 0, "max": 1, "mode": "auto"},
+        {"n": n_knots[1], "min": 0, "max": 1, "mode": "uniform"},
+    ],
+)
+@pytest.mark.parametrize(
+    "control_points", [torch.rand(n_ctrl_pts, n_ctrl_pts), None]
+)
+def test_forward(knots_u, knots_v, control_points):
+
+    # Define the model
+    model = SplineSurface(
+        orders=orders,
+        knots_u=knots_u,
+        knots_v=knots_v,
+        control_points=control_points,
+    )
+
+    # Evaluate the model
+    output_ = model(pts)
+    assert output_.shape == (pts.shape[0], 1)
+
+
+@pytest.mark.parametrize(
+    "knots_u",
+    [
+        torch.rand(n_knots[0]),
+        {"n": n_knots[0], "min": 0, "max": 1, "mode": "auto"},
+        {"n": n_knots[0], "min": 0, "max": 1, "mode": "uniform"},
+    ],
+)
+@pytest.mark.parametrize(
+    "knots_v",
+    [
+        torch.rand(n_knots[1]),
+        {"n": n_knots[1], "min": 0, "max": 1, "mode": "auto"},
+        {"n": n_knots[1], "min": 0, "max": 1, "mode": "uniform"},
+    ],
+)
+@pytest.mark.parametrize(
+    "control_points", [torch.rand(n_ctrl_pts, n_ctrl_pts), None]
+)
+def test_backward(knots_u, knots_v, control_points):
+
+    # Define the model
+    model = SplineSurface(
+        orders=orders,
+        knots_u=knots_u,
+        knots_v=knots_v,
+        control_points=control_points,
+    )
+
+    # Evaluate the model
+    output_ = model(pts)
+    loss = torch.mean(output_)
+    loss.backward()
+    assert model.control_points.grad.shape == model.control_points.shape