I have a `ParametricNDsolve`

solution for a differential equation for a huge system given by `s1`

. I have no problem in evaluating the value using the solution. It’s, in fact, speeding up the process using Parallel cores.

I have represented the variables by `a, b, c`

and `d`

, some of which are indexed by `j`

, and the parameters are `g, n1`

and `n2`

.

My attempt is to create a table of all the required variables and replace `s1`

to them as in `Tab1`

and finally evaluate `t`

shown by `Tab2`

```
Tab1 = Table( Table(Table({a(j, t)(g, n1, n2), b(j, t)(g, n1, n2),
c(g, n1, n2)(t), d(g, n1, n2)(t)}, {j, 1, 2}), {g, 1, 3, 1}), {n1, 1, 4}, {n2, 1, 4}) /. s1; (*first step*)
Tab2=Tab1/.t->12 (say)
```

So,`Tab1`

is only a replacement of the solution `s1`

to the given variables `a, b, c`

and `d`

, which takes me more time to evaluate than `Tab2`

. Is there any way I can parallel map or parallelEvaluate Tab1 using all cores available such that `s1`

is mapped to the table of variables simultaneously in parallel