diff --git a/include/rxmesh/util/inverse.h b/include/rxmesh/util/inverse.h
new file mode 100644
index 00000000..656710ba
--- /dev/null
+++ b/include/rxmesh/util/inverse.h
@@ -0,0 +1,42 @@
+#pragma once
+
+#include <cuda.h>
+#include "rxmesh/types.h"
+
+#include <Eigen/Dense>
+
+namespace rxmesh {
+/**
+ * @brief since 3x3 matrix inverse in eigen is buggy on device (it results into
+ * "unspecified launch failure"), we convert eigen matrix into glm, inverse it,
+ * then convert it back to eigen matrix
+ * @tparam T the floating point type of the matrix
+ * @tparam n the size of the matrix, expected/tested sizes are 2,3, and 4.
+ */
+
+template <typename T, int n>
+__device__ __host__ __inline__ Eigen::Matrix<T, n, n> inverse(
+    const Eigen::Matrix<T, n, n>& in)
+{
+    glm::mat<n, n, T, glm::defaultp> glm_mat;
+
+    for (int i = 0; i < n; ++i) {
+        for (int j = 0; j < n; ++j) {
+            glm_mat[i][j] = in(i, j);
+        }
+    }
+
+    auto glm_inv = glm::inverse(glm_mat);
+
+    Eigen::Matrix<T, n, n> eig_inv;
+
+    for (int i = 0; i < n; ++i) {
+        for (int j = 0; j < n; ++j) {
+            eig_inv(i, j) = glm_inv[i][j];
+        }
+    }
+    return eig_inv;
+}
+
+
+}  // namespace rxmesh
\ No newline at end of file
diff --git a/tests/RXMesh_test/test_scalar.cu b/tests/RXMesh_test/test_scalar.cu
index f4bda87a..ceac33f5 100644
--- a/tests/RXMesh_test/test_scalar.cu
+++ b/tests/RXMesh_test/test_scalar.cu
@@ -3,6 +3,8 @@
 #include "rxmesh/diff/scalar.h"
 
 #include "rxmesh/rxmesh_static.h"
+#include "rxmesh/util/inverse.h"
+
 
 #define RX_ASSERT_NEAR(val1, val2, eps, d_err)                           \
     if (abs(val1 - val2) > eps) {                                        \
@@ -749,7 +751,9 @@ __global__ static void test_min_quadratic(int* d_err, T eps = 1e-9)
                     2.0 * x[1] + 6.0 * x[2] + 10;
 
     // Solve for minimum
-    const Eigen::Vector<T, 3> x_min = -f.Hess.inverse() * f.grad;
+    typename Real3::HessType f_hess_inv = inverse(f.Hess);
+
+    const Eigen::Vector<T, 3> x_min = -f_hess_inv * f.grad;
 
     RX_ASSERT_NEAR(x_min.x(), -0.5, eps, d_err);
     RX_ASSERT_NEAR(x_min.y(), 0.5, eps, d_err);