update docs

README.md

@@ -146,7 +146,7 @@ X0 = Hyperrectangle(low=[0.9, -0.1], high=[1.1, 0.1])
 prob = @ivp(x' = duffing!(x), x(0) ∈ X0, dim=2)
 
 # solve using a Taylor model set representation
-sol = solve(prob, tspan=(0.0, 20*T), alg=TMJets21a())
+sol = solve(prob, tspan=(0.0, 20*T), alg=TMJets())
 
 # plot the flowpipe in state-space
 plot(sol, vars=(1, 2), xlab="x", ylab="v", lw=0.5, color=:red)

docs/src/lib/algorithms/TMJets.md

@@ -1,11 +1,5 @@
 ```@docs
 TMJets
-TMJets20
-TMJets21a
-TMJets21b
-ReachabilityAnalysis.iscontractive
-ReachabilityAnalysis.validated_step!
-ReachabilityAnalysis.absorb_remainder
-ReachabilityAnalysis.picard_remainder!
-ReachabilityAnalysis.remainder_taylorstep!
+TMJets1
+TMJets2
 ```

docs/src/tutorials/taylor_methods/introduction.md

@@ -51,7 +51,7 @@ fig = DisplayAs.Text(DisplayAs.PNG(fig))  # hide
 ```
 The divergence observed in the solution is due to using an algorithm which doesn't specialize for intervals hence suffers from dependency problems.
 
-However, specialized algorithms can handle this case without noticeable wrapping effect, producing a sequence of sets whose union covers the true solution for all initial points. We use the [`ReachabilityAnalysis.jl`](https://github.com/JuliaReach/ReachabilityAnalysis.jl) interface to the algorithm [`TMJets21a`](@ref), which is itself implemented in
+However, specialized algorithms can handle this case without noticeable wrapping effect, producing a sequence of sets whose union covers the true solution for all initial points. We use the [`ReachabilityAnalysis.jl`](https://github.com/JuliaReach/ReachabilityAnalysis.jl) interface to the algorithm [`TMJets1`](@ref), which is itself implemented in
 [`TaylorModels.jl`](https://github.com/JuliaIntervals/TaylorModels.jl).
 
 ```@example nonlinear_univariate
@@ -69,7 +69,7 @@ X0 = -1 .. 1
 prob = @ivp(x' = f(x), x(0) ∈ X0, dim=1)
 
 # solve it using a Taylor model method
-sol = solve(prob, alg=TMJets21a(abstol=1e-10), T=15)
+sol = solve(prob, alg=TMJets1(abstol=1e-10), T=15)
 
 # visualize the solution in time
 fig = plot(sol, vars=(0, 1), xlab="t", ylab="x(t)", title="Specialized (Taylor-model based) integrator")

src/Algorithms/TMJets/TMJets.jl

@@ -1,7 +1,7 @@
 """
     TMJets1{N, DM<:AbstractDisjointnessMethod} <: AbstractContinuousPost
 
-Validated integration using Taylor models.
+Validated integration using Taylor models with the `validated_integ` algorithm.
 
 ### Fields
 
@@ -45,9 +45,9 @@ rsetrep(::TMJets1{N}) where {N} = TaylorModelReachSet{N}
 
 
 """
-    TMJets1{N, DM<:AbstractDisjointnessMethod} <: AbstractContinuousPost
+    TMJets2{N, DM<:AbstractDisjointnessMethod} <: AbstractContinuousPost
 
-Validated integration using Taylor models.
+Validated integration using Taylor models with the `validated_integ2` algorithm.
 
 ### Fields