Merge pull request #217 from SciML/bumps

Version bumps

docs/Project.toml

@@ -50,40 +50,40 @@ Unitful = "1986cc42-f94f-5a68-af5c-568840ba703d"
 Zygote = "e88e6eb3-aa80-5325-afca-941959d7151f"
 
 [compat]
-AdvancedHMC = "0.5, 0.6"
+AdvancedHMC = "0.6"
 BenchmarkTools = "1"
 CSV = "0.10"
-CUDA = "4, 5"
-ComponentArrays = "0.14, 0.15"
-DataDrivenDiffEq = "1.2"
+CUDA = "5"
+ComponentArrays = "0.15"
+DataDrivenDiffEq = "1.4"
 DataDrivenSparse = "0.1"
 DataFrames = "1"
-DiffEqFlux = "2"
-DiffEqGPU = "2, 3"
+DiffEqFlux = "3"
+DiffEqGPU = "3"
 DifferentialEquations = "7"
 Distributions = "0.25"
 Documenter = "1"
 DomainSets = "0.6, 0.7"
 Flux = "0.13, 0.14"
 ForwardDiff = "0.10"
 IncompleteLU = "0.2"
-Integrals = "3, 4"
+Integrals = "4"
 LineSearches = "7"
 LinearSolve = "2"
 Lux = "0.5"
 MCMCChains = "6"
 Measurements = "2"
-MethodOfLines = "0.9, 0.10"
-ModelingToolkit = "8"
+MethodOfLines = "0.11"
+ModelingToolkit = "9.9"
 MultiDocumenter = "0.7"
-NeuralPDE = "5"
-NonlinearSolve = "1, 2"
+NeuralPDE = "5.15"
+NonlinearSolve = "3"
 Optimization = "3"
-OptimizationMOI = "0.1, 0.2"
-OptimizationNLopt = "0.1"
-OptimizationOptimJL = "0.1"
-OptimizationOptimisers = "0.1"
-OptimizationPolyalgorithms = "0.1, 0.2"
+OptimizationMOI = "0.4"
+OptimizationNLopt = "0.2"
+OptimizationOptimJL = "0.2"
+OptimizationOptimisers = "0.2"
+OptimizationPolyalgorithms = "0.2"
 OrdinaryDiffEq = "6"
 Plots = "1"
 SciMLExpectations = "2"

docs/src/getting_started/find_root.md

@@ -47,7 +47,7 @@ using ModelingToolkit, NonlinearSolve
 eqs = [0 ~ σ * (y - x),
  0 ~ x * (ρ - z) - y,
  0 ~ x * y - β * z]
-@named ns = NonlinearSystem(eqs, [x, y, z], [σ, ρ, β])
+@mtkbuild ns = NonlinearSystem(eqs, [x, y, z], [σ, ρ, β])
 
 # Convert the symbolic system into a numerical system
 prob = NonlinearProblem(ns, [])
@@ -56,7 +56,7 @@ prob = NonlinearProblem(ns, [])
 sol = solve(prob, NewtonRaphson())
 
 # Analyze the solution
-@show sol.u, prob.f(sol.u, prob.p)
+@show sol[[x,y,z]], sol.resid
 ```
 
 ## Step-by-Step Solution
@@ -122,7 +122,7 @@ Finally, we bring these pieces together, the equation along with its states and
 define our `NonlinearSystem`:
 
 ```@example first_rootfind
-@named ns = NonlinearSystem(eqs, [x, y, z], [σ, ρ, β])
+@mtkbuild ns = NonlinearSystem(eqs, [x, y, z], [σ, ρ, β])
 ```
 
 ### Step 3: Convert the Symbolic Problem to a Numerical Problem
@@ -178,5 +178,5 @@ We can check it as follows:
 
 ```@example first_rootfind
 # Analyze the solution
-@show sol.u, sol.resid
+@show sol[[x,y,z]], sol.resid
 ```

docs/src/getting_started/first_simulation.md

@@ -66,14 +66,11 @@ eqs = [D(x) ~ α * x - β * x * y
  z ~ x + y]
 
 # Bring these pieces together into an ODESystem with independent variable t
-@named sys = ODESystem(eqs, t)
-
-# Symbolically Simplify the System
-simpsys = structural_simplify(sys)
+@mtkbuild sys = ODESystem(eqs, t)
 
 # Convert from a symbolic to a numerical problem to simulate
 tspan = (0.0, 10.0)
-prob = ODEProblem(simpsys, [], tspan)
+prob = ODEProblem(sys, [], tspan)
 
 # Solve the ODE
 sol = solve(prob)
@@ -173,22 +170,15 @@ to represent an `ODESystem` with the following:
 
 ```@example first_sim
 # Bring these pieces together into an ODESystem with independent variable t
-@named sys = ODESystem(eqs, t)
+@mtkbuild sys = ODESystem(eqs, t)
 ```
 
-Next, we want to simplify this into a standard ODE system. Notice that in our equations
-we have an algebraic equation `z ~ x + y`. This is not a differential equation but an
-algebraic equation, and thus we call this set of equations a Differential-Algebraic Equation
-(DAE). The symbolic system of ModelingToolkit can eliminate such equations to return simpler
-forms to numerically approximate. Let's tell it to simplify the system using
-`structural_simplify`:
-
-```@example first_sim
-# Symbolically Simplify the System
-simpsys = structural_simplify(sys)
-```
+Notice that in our equations we have an algebraic equation `z ~ x + y`. This is not a 
+differential equation but an algebraic equation, and thus we call this set of equations a 
+Differential-Algebraic Equation (DAE). The symbolic system of ModelingToolkit can eliminate 
+such equations to return simpler forms to numerically approximate.
 
-Notice that what is returned is another `ODESystem`, but now with the simplified set of
+Notice that what is returned is an `ODESystem`, but now with the simplified set of
 equations. `z` has been turned into an “observable”, i.e. a state that is not computed
 but can be constructed on-demand. This is one of the ways that SciML reaches its speed:
 you can have 100,000 equations, but solve only 1,000 to then automatically reconstruct
@@ -213,7 +203,7 @@ like:
 ```@example first_sim
 # Convert from a symbolic to a numerical problem to simulate
 tspan = (0.0, 10.0)
-prob = ODEProblem(simpsys, [], tspan)
+prob = ODEProblem(sys, [], tspan)
 ```
 
 ### Step 4: Solve the ODE System
@@ -282,9 +272,8 @@ D = Differential(t)
 eqs = [D(🐰) ~ α * 🐰 - β * 🐰 * 🐺,
  D(🐺) ~ -γ * 🐺 + δ * 🐰 * 🐺]
 
-@named sys = ODESystem(eqs, t)
-simpsys = structural_simplify(sys)
-prob = ODEProblem(simpsys, [], (0.0, 10.0))
+@mtkbuild sys = ODESystem(eqs, t)
+prob = ODEProblem(sys, [], (0.0, 10.0))
 sol = solve(prob)
 ```