diff --git a/pina/solver/physics_informed_solver/competitive_pinn.py b/pina/solver/physics_informed_solver/competitive_pinn.py
index 058c53f40..18a9861e9 100644
--- a/pina/solver/physics_informed_solver/competitive_pinn.py
+++ b/pina/solver/physics_informed_solver/competitive_pinn.py
@@ -103,9 +103,6 @@ def __init__(
             loss=loss,
         )
 
-        # Set automatic optimization to False
-        self.automatic_optimization = False
-
     def forward(self, x):
         """
         Forward pass.
diff --git a/pina/solver/physics_informed_solver/self_adaptive_pinn.py b/pina/solver/physics_informed_solver/self_adaptive_pinn.py
index a6310d515..d9c9a9bb3 100644
--- a/pina/solver/physics_informed_solver/self_adaptive_pinn.py
+++ b/pina/solver/physics_informed_solver/self_adaptive_pinn.py
@@ -158,9 +158,6 @@ def __init__(
             loss=loss,
         )
 
-        # Set automatic optimization to False
-        self.automatic_optimization = False
-
         self._vectorial_loss = deepcopy(self.loss)
         self._vectorial_loss.reduction = "none"
 
diff --git a/pina/solver/solver.py b/pina/solver/solver.py
index 2a173b33d..6776fea9d 100644
--- a/pina/solver/solver.py
+++ b/pina/solver/solver.py
@@ -14,9 +14,13 @@
 
 class SolverInterface(lightning.pytorch.LightningModule, metaclass=ABCMeta):
     """
-    Abstract base class for PINA solvers. All specific solvers should inherit
-    from this interface. This class is a wrapper of
-    :class:`~lightning.pytorch.LightningModule`.
+    Abstract base class for PINA solvers. All specific solvers must inherit
+    from this interface. This class extends
+    :class:`~lightning.pytorch.core.LightningModule`, providing additional
+    functionalities for defining and optimizing Deep Learning models.
+
+    By inheriting from this base class, solvers gain access to built-in training
+    loops, logging utilities, and optimization techniques.
     """
 
     def __init__(self, problem, weighting, use_lt):
@@ -442,6 +446,14 @@ def __init__(
         :param bool use_lt: If ``True``, the solver uses LabelTensors as input.
         :raises ValueError: If the models are not a list or tuple with length
             greater than one.
+
+        .. warning::
+            :class:`MultiSolverInterface` uses manual optimization by setting
+            ``automatic_optimization=False`` in
+            :class:`~lightning.pytorch.core.LightningModule`. For more
+            information on manual optimization please
+            see `here <https://lightning.ai/docs/pytorch/stable/\
+                model/manual_optimization.html>`_.
         """
         if not isinstance(models, (list, tuple)) or len(models) < 2:
             raise ValueError(
@@ -450,6 +462,16 @@ def __init__(
                 "one."
             )
 
+        if optimizers is None:
+            optimizers = [
+                self.default_torch_optimizer() for _ in range(len(models))
+            ]
+
+        if schedulers is None:
+            schedulers = [
+                self.default_torch_scheduler() for _ in range(len(models))
+            ]
+
         if any(opt is None for opt in optimizers):
             optimizers = [
                 self.default_torch_optimizer() if opt is None else opt
@@ -480,12 +502,23 @@ def __init__(
                 f"Got {len(models)} models, and {len(optimizers)}"
                 " optimizers."
             )
+        if len(schedulers) != len(optimizers):
+            raise ValueError(
+                "You must define one scheduler for each optimizer."
+                f"Got {len(schedulers)} schedulers, and {len(optimizers)}"
+                " optimizers."
+            )
 
         # initialize the model
         self._pina_models = torch.nn.ModuleList(models)
         self._pina_optimizers = optimizers
         self._pina_schedulers = schedulers
 
+        # Set automatic optimization to False.
+        # For more information on manual optimization see:
+        # http://lightning.ai/docs/pytorch/stable/model/manual_optimization.html
+        self.automatic_optimization = False
+
     def configure_optimizers(self):
         """
         Optimizer configuration for the solver.