diff --git a/deepxde/icbc/boundary_conditions.py b/deepxde/icbc/boundary_conditions.py
index 85732f4a4..27ddea6e5 100644
--- a/deepxde/icbc/boundary_conditions.py
+++ b/deepxde/icbc/boundary_conditions.py
@@ -248,19 +248,41 @@ class PointSetOperatorBC:
             and outputs a tensor of size `N x 1`, where `N` is the length of
             `inputs`. `inputs` and `outputs` are the network input and output
             tensors, respectively; `X` are the NumPy array of the `inputs`.
+        batch_size: The number of points per minibatch, or `None` to return all points.
+            This is only supported for the backend PyTorch and PaddlePaddle.
+            Note, If you want to use batch size here, you should also set callback
+            'dde.callbacks.PDEPointResampler(bc_points=True)' in training.
+        shuffle: Randomize the order on each pass through the data when batching.
     """
 
-    def __init__(self, points, values, func):
+    def __init__(self, points, values, func, batch_size=None, shuffle=True):
         self.points = np.array(points, dtype=config.real(np))
         if not isinstance(values, numbers.Number) and values.shape[1] != 1:
             raise RuntimeError("PointSetOperatorBC should output 1D values")
         self.values = bkd.as_tensor(values, dtype=config.real(bkd.lib))
         self.func = func
+        self.batch_size = batch_size
+
+        if batch_size is not None:  # batch iterator and state
+            if backend_name not in ["pytorch", "paddle"]:
+                raise RuntimeError(
+                    "batch_size only implemented for pytorch and paddle backend"
+                )
+            self.batch_sampler = data.sampler.BatchSampler(len(self), shuffle=shuffle)
+            self.batch_indices = None
+
+    def __len__(self):
+        return self.points.shape[0]
 
     def collocation_points(self, X):
+        if self.batch_size is not None:
+            self.batch_indices = self.batch_sampler.get_next(self.batch_size)
+            return self.points[self.batch_indices]
         return self.points
 
     def error(self, X, inputs, outputs, beg, end, aux_var=None):
+        if self.batch_size is not None:
+            return self.func(inputs, outputs, X)[beg:end] - self.values[self.batch_indices]
         return self.func(inputs, outputs, X)[beg:end] - self.values