diff --git a/torchquad/integration/grid_integrator.py b/torchquad/integration/grid_integrator.py
index 2243399d..72350371 100644
--- a/torchquad/integration/grid_integrator.py
+++ b/torchquad/integration/grid_integrator.py
@@ -28,7 +28,7 @@ def f(integration_domain, N, requires_grad=False, backend=None):
     def _weights(self, N, dim, backend, requires_grad=False):
         return None
 
-    def integrate(self, fn, dim, N, integration_domain, backend):
+    def integrate(self, fn, dim, N, integration_domain, backend, args=None):
         """Integrate the passed function on the passed domain using a Composite Newton Cotes rule.
         The argument meanings are explained in the sub-classes.
 
@@ -47,7 +47,7 @@ def integrate(self, fn, dim, N, integration_domain, backend):
 
         logger.debug("Evaluating integrand on the grid.")
         function_values, num_points = self.evaluate_integrand(
-            fn, grid_points, weights=self._weights(n_per_dim, dim, backend)
+            fn, grid_points, weights=self._weights(n_per_dim, dim, backend), args=args
         )
         self._nr_of_fevals = num_points
 
@@ -139,7 +139,7 @@ def _adjust_N(dim, N):
         return N
 
     def get_jit_compiled_integrate(
-        self, dim, N=None, integration_domain=None, backend=None
+        self, dim, N=None, integration_domain=None, backend=None, args=None
     ):
         """Create an integrate function where the performance-relevant steps except the integrand evaluation are JIT compiled.
         Use this method only if the integrand cannot be compiled.
@@ -151,6 +151,7 @@ def get_jit_compiled_integrate(
             N (int, optional): Total number of sample points to use for the integration. See the integrate method documentation for more details.
             integration_domain (list or backend tensor, optional): Integration domain, e.g. [[-1,1],[0,1]]. Defaults to [-1,1]^dim. It can also determine the numerical backend.
             backend (string, optional): Numerical backend. Defaults to integration_domain's backend if it is a tensor and otherwise to the backend from the latest call to set_up_backend or "torch" for backwards compatibility.
+            args (list or tuple, optional): Any arguments required by the function. Defaults to None.
 
         Returns:
             function(fn, integration_domain): JIT compiled integrate function where all parameters except the integrand and domain are fixed
@@ -197,7 +198,7 @@ def get_jit_compiled_integrate(
             def compiled_integrate(fn, integration_domain):
                 grid_points, hs, n_per_dim = jit_calculate_grid(N, integration_domain)
                 function_values, _ = self.evaluate_integrand(
-                    fn, grid_points, weights=self._weights(n_per_dim, dim, backend)
+                    fn, grid_points, weights=self._weights(n_per_dim, dim, backend), args=args
                 )
                 return jit_calculate_result(
                     function_values, dim, int(n_per_dim), hs, integration_domain
@@ -238,6 +239,7 @@ def step3(function_values, hs, integration_domain):
                     example_integrand,
                     grid_points,
                     weights=self._weights(n_per_dim, dim, backend),
+                    args=args,
                 )
 
                 # Trace the third step
@@ -257,7 +259,7 @@ def step3(function_values, hs, integration_domain):
                 def compiled_integrate(fn, integration_domain):
                     grid_points, hs, _ = step1(integration_domain)
                     function_values, _ = self.evaluate_integrand(
-                        fn, grid_points, weights=self._weights(n_per_dim, dim, backend)
+                        fn, grid_points, weights=self._weights(n_per_dim, dim, backend), args=args
                     )
                     result = step3(function_values, hs, integration_domain)
                     return result