I am trying to solve the same model discussed in this question using a PINN approach.

Frankenstining together some code based on the examples in the documentation, I ended up with the following:

```
using NeuralPDE, Flux, ModelingToolkit, GalacticOptim, Optim, DiffEqFlux, DifferentialEquations
using Quadrature,Cubature
import ModelingToolkit: Interval, infimum, supremum
@parameters t, x
@variables c₁(..), c₂(..)
Dt = Differential(t)
Dxx = Differential(x)^2
f = 1.2
m = 190
q = 0.001
ϵ₁ = 0.01
ϵ = 0.01
K = 1000
Θ = 0.0025
d₀ = 0.01
D₁ = 1
eqs = [Dt(c₁(t,x)) ~ 1/ϵ*(f*c₂(t,x)*(q-c₁(t,x))/(q+c₁(t,x)) + c₁(t,x)*(1-m*c₂(t,x))/(1-m*c₂(t,x) + ϵ₁) - c₁(t,x)^2) + D₁*Dxx(c₁(t,x)),
Dt(c₂(t,x)) ~ c₁(t,x)*(1-m*c₂(t,x)/(1-m*c₂(t,x) + ϵ₁)) - c₂(t,x) + D₁/d₀*(1 + (K-1)/(1 + 3/Θ))^(-3/2)*Dxx(c₂(t,x))]
bcs = [c₁(0,x) ~ rand(),
c₂(0,x) ~ rand(),
c₁(t,0) ~ 0.,
c₂(t,0) ~ 0.,
c₁(t,30) ~ 0.,
c₂(t,30) ~ 0.]
# Space and time domains
domains = [t ∈ Interval(0.0,0.3),
x ∈ Interval(0.0,30.0)]
# Neural network
input_ = length(domains)
n = 15
chain =[FastChain(FastDense(input_,n,Flux.σ),FastDense(n,n,Flux.σ),FastDense(n,1)) for _ in 1:3]
initθ = map(c -> Float64.(c), DiffEqFlux.initial_params.(chain))
_strategy = QuadratureTraining()
discretization = PhysicsInformedNN(chain, _strategy, init_params= initθ)
@named pde_system = PDESystem(eqs,bcs,domains,[t,x],[c₁(t, x),c₂(t, x)])
prob = discretize(pde_system,discretization)
sym_prob = symbolic_discretize(pde_system,discretization)
pde_inner_loss_functions = prob.f.f.loss_function.pde_loss_function.pde_loss_functions.contents
bcs_inner_loss_functions = prob.f.f.loss_function.bcs_loss_function.bc_loss_functions.contents
cb = function (p,l)
println("loss: ", l )
println("pde_losses: ", map(l_ -> l_(p), pde_inner_loss_functions))
println("bcs_losses: ", map(l_ -> l_(p), bcs_inner_loss_functions))
return false
end
res = GalacticOptim.solve(prob,BFGS(); cb = cb, maxiters=5000)
phi = discretization.phi
using Plots
ts,xs = [infimum(d.domain):0.01:supremum(d.domain) for d in domains]
acum = [0;accumulate(+, length.(initθ))]
sep = [acum[i]+1 : acum[i+1] for i in 1:length(acum)-1]
minimizers_ = [res.minimizer[s] for s in sep]
u_predict = [[phi[i]([t,x],minimizers_[i])[1] for t in ts for x in xs] for i in 1:2]
for i in 1:2
p2 = plot(ts, xs, u_predict[i],linetype=:contourf,title = "predict");
plot(p2)
savefig("sol_c$i")
end
```

It runs fine but the result is wrong, I think the initial condition is incorrect. How do I use some random noise as the initial condition?