Update to MTK v9

SciML · Feb 27, 2024 · 752138b · 752138b
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commit 752138b
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Showing 8 changed files with 29 additions and 41 deletions.
diff --git a/docs/Project.toml b/docs/Project.toml
@@ -17,7 +17,7 @@ DataInterpolations = "4"
 DifferentialEquations = "7"
 Documenter = "1"
 ForwardDiff = "0.10"
-ModelingToolkit = "8"
+ModelingToolkit = "9"
 ModelingToolkitStandardLibrary = "2"
 OrdinaryDiffEq = "6"
 Plots = "1"

diff --git a/docs/src/lectures/lecture1.jl b/docs/src/lectures/lecture1.jl
@@ -157,7 +157,7 @@ eqs = [
     D(ẋ) ~ ẍ
     d*ẋ + k*x^1.5 ~ F
 ]
-@named odesys = ODESystem(eqs, t, vars, [])
+@mtkbuild odesys = ODESystem(eqs, t, vars, [])
 sys = structural_simplify(odesys)
 prob = ODEProblem(sys, [], (0.0, 0.01))
 sol = solve(prob; abstol=tol)
@@ -507,7 +507,7 @@ eqs =[
     D(x) ~ (F - k*x^1.5)/d
 ]
 
-@named odesys = ODESystem(eqs, t, vars, [])
+@mtkbuild odesys = ODESystem(eqs, t, vars, [])
 
 aesys = DAE2AE.dae_to_ae(odesys, Δt)
 sys = structural_simplify(aesys)

diff --git a/docs/src/lectures/lecture1.md b/docs/src/lectures/lecture1.md
@@ -185,8 +185,7 @@ ModelingToolkit.jl uses symbolic math from Symbolics.jl to provide automatic ind
 
 ```@example l1
 using ModelingToolkit
-@variables t
-D = Differential(t)
+using ModelingToolkit: t_nounits as t, D_nounits as D
 nothing # hide
 ```
 
@@ -213,8 +212,7 @@ nothing # hide
 Note the variables are defined as a function of the independent variable `t` and given initial conditions which are captured in the variable `vars`.  The equations are then defined using the tilde `~` operator, which represents the equation equality.  This information is then fed to an `ODESystem` constructor and simplified using the `structural_simplify` function.  
 
 ```@example l1
-@named odesys = ODESystem(eqs, t, vars, pars)
-sys = structural_simplify(odesys)
+@mtkbuild odesys = ODESystem(eqs, t, vars, pars)
 ```
 
 As can be seen, the 3 equation system is simplified down to 1 equation.  To see the solved states and equations we can use the respective functions
@@ -586,7 +584,7 @@ eqs = [
     D(dx) ~ ddx
     m*ddx + d*dx + k*x ~ F
 ]
-@named odesys = ODESystem(eqs, t, vars, pars)
+@mtkbuild odesys = ODESystem(eqs, t, vars, pars)
 sys = structural_simplify(odesys)
 prob = ODEProblem(sys, [], (0,10))
 sol = solve(prob)

diff --git a/docs/src/lectures/lecture2.jl b/docs/src/lectures/lecture2.jl
@@ -67,22 +67,19 @@ eqs_x = [
     x ~ x_fun(t,amp,f)
 ]
 
-@named odesys_x = ODESystem(eqs_x, t, vars, pars)
-sys_x = structural_simplify(odesys_x)
+@mtkbuild odesys_x = ODESystem(eqs_x, t, vars, pars)
 prob_x = ODEProblem(sys_x, [], (0, t_end))
 sol_x = solve(prob_x; saveat=time)
 
-@named odesys_ṁ1 = ODESystem(eqs_ṁ1, t, vars, pars)
-sys_ṁ1 = structural_simplify(odesys_ṁ1)
+@mtkbuild odesys_ṁ1 = ODESystem(eqs_ṁ1, t, vars, pars)
 u0 = [sol_x[s][1] for s in states(sys_ṁ1)]
 prob_ṁ1 = ODEProblem(sys_ṁ1, u0, (0, t_end))
 sol_ṁ1 = solve(prob_ṁ1)
 
 plot(sol_ṁ1; idxs=ṁ, label="guess", ylabel="ṁ")
 plot!(sol_x; idxs=ṁ, label="solution")
 
-@named odesys_ṁ2 = ODESystem(eqs_ṁ2, t, vars, pars)
-sys_ṁ2 = structural_simplify(odesys_ṁ2)
+@mtkbuild odesys_ṁ2 = ODESystem(eqs_ṁ2, t, vars, pars)
 prob_ṁ2 = ODEProblem(sys_ṁ2, u0, (0, t_end))
 sol_ṁ2 = solve(prob_ṁ2)
 

diff --git a/docs/src/lectures/lecture2.md b/docs/src/lectures/lecture2.md
@@ -157,9 +157,7 @@ using ModelingToolkit
 using DifferentialEquations
 using Symbolics
 using Plots
-
-@parameters t
-D = Differential(t)
+using ModelingToolkit: t_nounits as t, D_nounits as D
 
 # parameters -------
 pars = @parameters begin
@@ -253,8 +251,7 @@ nothing # hide
 Now we have 3 sets of equations, let's construct the systems and solve.  If we start with case 3 with the target ``x`` input, notice that the `structural_simplify` step outputs a system with 0 equations!
 
 ```@example l2
-@named odesys_x = ODESystem(eqs_x, t, vars, pars)
-sys_x = structural_simplify(odesys_x)
+@mtkbuild odesys_x = ODESystem(eqs_x, t, vars, pars)
 nothing # hide
 ```
 
@@ -282,8 +279,7 @@ plot(sol_x; idxs=ṁ)
 Now let's solve the other system and compare the results. 
 
 ```@example l2
-@named odesys_ṁ1 = ODESystem(eqs_ṁ1, t, vars, pars)
-sys_ṁ1 = structural_simplify(odesys_ṁ1)
+@mtkbuild odesys_ṁ1 = ODESystem(eqs_ṁ1, t, vars, pars)
 nothing # hide
 ```
 
@@ -310,8 +306,7 @@ plot!(sol_x; idxs=ṁ, label="solution")
 If we now solve for case 2, we can study the impact the compressibility derivation
 
 ```@example l2
-@named odesys_ṁ2 = ODESystem(eqs_ṁ2, t, vars, pars)
-sys_ṁ2 = structural_simplify(odesys_ṁ2)
+@mtkbuild odesys_ṁ2 = ODESystem(eqs_ṁ2, t, vars, pars)
 prob_ṁ2 = ODEProblem(sys_ṁ2, u0, (0, t_end))
 @time sol_ṁ2 = solve(prob_ṁ2);
 nothing # hide

diff --git a/docs/src/lectures/lecture6.md b/docs/src/lectures/lecture6.md
@@ -62,7 +62,7 @@ The pendulum problem as described above is derived assuming the following:
 If we attempt to solve this system we can see that it only solves up to the point that `x` crosses 0.
 
 ```@example l6
-sys = structural_simplify(pendulum)
+sys = complete(structural_simplify(pendulum))
 prob = ODEProblem(sys, ModelingToolkit.missing_variable_defaults(sys), (0, 10))
 sol = solve(prob)# gives retcode: DtLessThanMin
 plot(sol; idxs=[x,y])

diff --git a/docs/src/lectures/lecture7.md b/docs/src/lectures/lecture7.md
@@ -13,6 +13,7 @@ are not enough constraints to uniquely determine ``x`` and ``y``. Let's see how
 numerical solvers and ModelingToolkit behave.
 ```@example l7
 using DifferentialEquations, Sundials, ModelingToolkit, Plots, LinearAlgebra
+using ModelingToolkit: t_nounits as t, D_nounits as D
 
 function f!(out, du, u, p, t)
     # u[1]: x, du[1]: x'
@@ -32,8 +33,8 @@ step. We call such systems non-integrable or singular. Let's try to use
 ModelingToolkit to analyze this problem.
 
 ```@example l7
-@variables t x(t) y(t) z(t)
-D = Differential(t)
+@variables x(t) y(t) z(t)
+
 eqs = [
     x + y ~ sin(t)
     z ~ sin(t)

diff --git a/docs/src/lectures/lecture8.md b/docs/src/lectures/lecture8.md
@@ -57,6 +57,7 @@ we cannot break the nonlinear system into smaller subsystems. Let's implement
 the differentiated system and solve it numerically.
 ```@example l8
 using ModelingToolkit, OrdinaryDiffEq, Plots, LinearAlgebra
+using ModelingToolkit: t_nounits as t, D_nounits as D
 
 function pend_manual(out, u, g, t)
     ẋ, x, ẏ, y, λ = u
@@ -245,14 +246,14 @@ system. We can implement the same system in ModelingToolkit and see the same
 behavior.
 ```@example l8
 @parameters g
-@variables t x(t) y(t) [state_priority = 10] λ(t)
-D = Differential(t)
+@variables x(t) y(t) [state_priority = 10] λ(t)
+
 eqs = [
        D(D(x)) ~ λ * x
        D(D(y)) ~ λ * y - g
        x^2 + y^2 ~ 1
       ]
-@named pend = ODESystem(eqs)
+@named pend = ODESystem(eqs,t)
 pend = complete(pend)
 ss = structural_simplify(pend)
 prob_ir = ODEProblem(ss, [ModelingToolkit.missing_variable_defaults(ss); x => 1], (0.0, 25.0), [g => 1])
@@ -274,8 +275,7 @@ To save the hassle of running algorithms by hand, we can just implement the new
 system in ModelingToolkit.
 ```@example l8
 @parameters g
-@variables t x(t) y(t) λ(t) θ(t) [state_priority = 10] T(t) V(t) E(t)
-D = Differential(t)
+@variables x(t) y(t) λ(t) θ(t) [state_priority = 10] T(t) V(t) E(t)
 eqs = [
        D(D(x)) ~ λ * x
        D(D(y)) ~ λ * y - g
@@ -285,7 +285,7 @@ eqs = [
        V ~ y * g
        E ~ T + V
       ]
-@named pend = ODESystem(eqs)
+@named pend = ODESystem(eqs,t)
 pend = complete(pend)
 ss = structural_simplify(pend)
 prob_ir = ODEProblem(ss, ModelingToolkit.missing_variable_defaults(ss), (0.0, 25.0), [g => 1])
@@ -313,9 +313,8 @@ balancing algorithm is available in the original dummy derivative paper
 
 ```@example l8
 @parameters g
-@variables t x1(t) x2(t) y1(t) y2(t) λ1(t) λ2(t) θ1(t) [state_priority = 10] θ2(t) [state_priority = 10]
+@variables x1(t) x2(t) y1(t) y2(t) λ1(t) λ2(t) θ1(t) [state_priority = 10] θ2(t) [state_priority = 10]
 @variables T(t) V(t) lx2(t) ly2(t)
-D = Differential(t)
 eqs = [
        D(D(x1)) ~ λ1 * x1 - λ2 * lx2
        D(D(y1)) ~ λ1 * y1 - λ2 * ly2 - g
@@ -331,7 +330,7 @@ eqs = [
        V ~ y1 * g + y2 * g
       ]
 
-@named pend = ODESystem(eqs)
+@named pend = ODESystem(eqs,t)
 pend = complete(pend)
 ss = structural_simplify(pend)
 prob_ir = ODEProblem(ss,
@@ -516,9 +515,7 @@ using ModelingToolkit, OrdinaryDiffEq, LinearAlgebra
 import ModelingToolkitStandardLibrary.Hydraulic.IsothermalCompressible as IC
 import ModelingToolkitStandardLibrary.Blocks as B
 import ModelingToolkitStandardLibrary.Mechanical.Translational as T
-
-@parameters t
-D = Differential(t)
+using ModelingToolkit: t_nounits as t, D_nounits as D
 
 function System(use_input, f; name)
     @parameters t
@@ -630,10 +627,10 @@ end
 
 ```julia
 using ModelingToolkit, OrdinaryDiffEq, Plots
+using ModelingToolkit: t_nounits as t, D_nounits as D
 
 @parameters g
-@variables t x1(t) x2(t) y1(t) y2(t) λ1(t) λ2(t) θ1(t) [state_priority = 10] θ2(t) [state_priority = 10]
-D = Differential(t)
+@variables x1(t) x2(t) y1(t) y2(t) λ1(t) λ2(t) θ1(t) [state_priority = 10] θ2(t) [state_priority = 10]
 eqs = [
        D(D(x1)) ~ λ1 * x1,
        D(D(y1)) ~ λ1 * y1 - g,
@@ -645,7 +642,7 @@ eqs = [
        y2 ~ y1 + sin(θ2),
       ]
 
-@named pend = ODESystem(eqs)
+@named pend = ODESystem(eqs,t)
 pend = complete(pend)
 ss = structural_simplify(pend)
 prob_ir = ODEProblem(ss,