# Discrepancy between pybamm inner and numpy inner

**URL:** <https://pybamm.discourse.group/t/discrepancy-between-pybamm-inner-and-numpy-inner/331>\
**Category:** General\
**Created:** [27 May 2025 19:28 UTC](https://pybamm.discourse.group/t/discrepancy-between-pybamm-inner-and-numpy-inner/331 "2025-05-27T19:28:40Z")\
**Posts on this page:** 1\
**Page:** 1

<div class="post-metadata">

**Author:** ![derek](https://avatars.discourse-cdn.com/v4/letter/d/b38774/32.png) [@derek](https://pybamm.discourse.group/u/derek)\
**Post date:** [27 May 2025 19:28 UTC](https://pybamm.discourse.group/t/discrepancy-between-pybamm-inner-and-numpy-inner/331/1 "2025-05-27T19:28:40Z")

</div>

I was wondering the reasoning for this discrepancy between the Pybamm Inner product and the numpy inner product

```auto
import pybamm
import numpy as np

a = np.array([1,2,3,4,5])
b = np.array([5,4,3,2,1])

c_pb = pybamm.Inner(a,b)
c_np = np.inner(a,b)
print(f"pybamm inner: {c_pb.evaluate()} \n numpy inner: {c_np}")

```

returns:

```auto
pybamm inner: [[5.]
 [8.]
 [9.]
 [8.]
 [5.]] 
 numpy inner: 35

```

I am attempting to apply the inner product as a part of a custom parameter model evaluation which would utilize custom Input Parameters representing the GP model, it would be very helpful if these models are able to be solved in parallel. Any help is appreciated thank you!