Removed dx and dt

docs/src/tutorials/constraints.md

@@ -35,8 +35,6 @@ Dxx = Differential(x)^2
 _σ = 0.5
 x_0 = -2.2
 x_end = 2.2
-# Discretization
-dx = 0.01
 
 eq = Dx((α * x - β * x^3) * p(x)) ~ (_σ^2 / 2) * Dxx(p(x))
 
@@ -57,7 +55,7 @@ lb = [x_0]
 ub = [x_end]
 function norm_loss_function(phi, θ, p)
  function inner_f(x, θ)
- dx * phi(x, θ) .- 1
+ 0.01 * phi(x, θ) .- 1
  end
  prob = IntegralProblem(inner_f, lb, ub, θ)
  norm2 = solve(prob, HCubatureJL(), reltol = 1e-8, abstol = 1e-8, maxiters = 10)
@@ -98,7 +96,7 @@ using Plots
 C = 142.88418699042 #fitting param
 analytic_sol_func(x) = C * exp((1 / (2 * _σ^2)) * (2 * α * x^2 - β * x^4))
 
-xs = [infimum(d.domain):dx:supremum(d.domain) for d in domains][1]
+xs = [infimum(d.domain):0.01:supremum(d.domain) for d in domains][1]
 u_real = [analytic_sol_func(x) for x in xs]
 u_predict = [first(phi(x, res.u)) for x in xs]
 

docs/src/tutorials/param_estim.md

@@ -58,7 +58,7 @@ u0 = [1.0; 0.0; 0.0]
 tspan = (0.0, 1.0)
 prob = ODEProblem(lorenz!, u0, tspan)
 sol = solve(prob, Tsit5(), dt = 0.1)
-ts = [infimum(d.domain):dt:supremum(d.domain) for d in domains][1]
+ts = [infimum(d.domain):0.01:supremum(d.domain) for d in domains][1]
 function getData(sol)
  data = []
  us = hcat(sol(ts).u...)
@@ -130,7 +130,7 @@ And then finally some analysis by plotting.
 
 ```@example param_estim
 minimizers = [res.u.depvar[depvars[i]] for i in 1:3]
-ts = [infimum(d.domain):(dt / 10):supremum(d.domain) for d in domains][1]
+ts = [infimum(d.domain):(0.001):supremum(d.domain) for d in domains][1]
 u_predict = [[discretization.phi[i]([t], minimizers[i])[1] for t in ts] for i in 1:3]
 plot(sol)
 plot!(ts, u_predict, label = ["x(t)" "y(t)" "z(t)"])