diff --git a/src/bayesvalidrox/surrogate_models/exp_designs.py b/src/bayesvalidrox/surrogate_models/exp_designs.py
index 2caa086e5e1bace1b7ab5eee9671e4c27b633ef8..94f41469cf251e95e4962a066ac8b6ffb604794c 100644
--- a/src/bayesvalidrox/surrogate_models/exp_designs.py
+++ b/src/bayesvalidrox/surrogate_models/exp_designs.py
@@ -413,9 +413,9 @@ class ExpDesigns:
 
             elif 'lognorm' in dist_type.lower():
                 polytype = 'hermite'
-                Mu = np.log(params[0]**2/np.sqrt(params[0]**2 + params[1]**2))
-                Sigma = np.sqrt(np.log(1 + params[1]**2 / params[0]**2))
-                dist = chaospy.LogNormal(mu=Mu, sigma=Sigma)
+                # Mu = np.log(params[0]**2/np.sqrt(params[0]**2 + params[1]**2))
+                # Sigma = np.sqrt(np.log(1 + params[1]**2 / params[0]**2))
+                dist = chaospy.LogNormal(mu=params[0], sigma=params[1])
 
             elif 'expon' in dist_type.lower():
                 polytype = 'arbitrary'
@@ -478,7 +478,10 @@ class ExpDesigns:
                 # store the raw data with given random indices
                 samples[:, pa_idx] = self.raw_data[pa_idx, rand_idx]
         else:
-            samples = self.JDist.resample(int(n_samples)).T
+            try:
+                samples = self.JDist.resample(int(n_samples)).T
+            except AttributeError:
+                samples = self.JDist.sample(int(n_samples)).T
             # Check if all samples are in the bound_tuples
             for idx, param_set in enumerate(samples):
                 if not self._check_ranges(param_set, self.bound_tuples):