Add more ODE benchmarks

Project.toml

@@ -4,9 +4,11 @@ authors = ["Utkarsh <[email protected]> and contributors"]
 version = "1.0.0-DEV"
 
 [deps]
+BenchmarkTools = "6e4b80f9-dd63-53aa-95a3-0cdb28fa8baf"
 CUDA = "052768ef-5323-5732-b1bb-66c8b64840ba"
 DiffEqGPU = "071ae1c0-96b5-11e9-1965-c90190d839ea"
 MLDatasets = "eb30cadb-4394-5ae3-aed4-317e484a6458"
+ParameterizedFunctions = "65888b18-ceab-5e60-b2b9-181511a3b968"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 SciMLBase = "0bca4576-84f4-4d90-8ffe-ffa030f20462"
 Setfield = "efcf1570-3423-57d1-acb7-fd33fddbac46"

examples/ode_estimation/Lotka_Volterra/lorenz.jl

@@ -0,0 +1,74 @@
+using ParameterizedFunctions, OrdinaryDiffEq, Optimization
+using OptimizationBBO, Plots, ForwardDiff, BenchmarkTools
+using StaticArrays
+
+g1 = @ode_def LorenzExample begin
+    dx = σ*(y-x)
+    dy = x*(ρ-z) - y
+    dz = x*y - β*z
+end σ ρ β
+p = [10.0,28.0,2.66] # Parameters used to construct the dataset
+r0 = [1.0; 0.0; 0.0]                #[-11.8,-5.1,37.5] PODES Initial values of the system in space # [0.1, 0.0, 0.0]
+tspan = (0.0, 30.0)                 # PODES sample of 3000 observations over the (0,30) timespan
+prob = ODEProblem(g1, r0, tspan,p)
+tspan2 = (0.0, 3.0)                 # Xiang test sample of 300 observations with a timestep of 0.01
+prob_short = ODEProblem(g1, r0, tspan2,p)
+
+dt = 30.0/3000
+tf = 30.0
+tinterval = 0:dt:tf
+t  = collect(tinterval)
+
+h = 0.01
+M = 300
+tstart = 0.0
+tstop = tstart + M * h
+tinterval_short = 0:h:tstop
+t_short = collect(tinterval_short)
+
+# Generate Data
+data_sol_short = solve(prob_short,Vern9(),saveat=t_short,reltol=1e-9,abstol=1e-9)
+data_short = convert(Array, data_sol_short) # This operation produces column major dataset obs as columns, equations as rows
+data_sol = solve(prob,Vern9(),saveat=t,reltol=1e-9,abstol=1e-9)
+sizesol = size(data_sol)
+data = Array(data_sol)
+
+plot(data_sol_short,vars=(1,2,3)) # the short solution
+plot(data_sol,vars=(1,2,3)) # the longer solution
+interpolation_sol = solve(prob,Vern7(),saveat=t,reltol=1e-12,abstol=1e-12)
+plot(interpolation_sol,vars=(1,2,3))
+
+function loss(u, p)
+    odeprob, t = p
+    prob = remake(odeprob; p = u)
+    pred = Array(solve(prob, Vern9(), saveat = t))
+    sum(abs2, data .- pred)
+end
+
+Xiang2015Bounds = Tuple{Float64, Float64}[(9, 11), (20, 30), (2, 3)] # for local optimizations
+xlow_bounds = @SVector [9.0,20.0,2.0]
+xhigh_bounds = @SVector [11.0,30.0,3.0]
+LooserBounds = Tuple{Float64, Float64}[(0, 22), (0, 60), (0, 6)] # for global optimization
+GloIniPar = @SVector [0.0, 0.5, 0.1] # for global optimizations
+LocIniPar = @SVector [9.0, 20.0, 2.0] # for local optimization
+
+optprob = OptimizationProblem(loss, LocIniPar, (prob, t), lb = xlow_bounds, ub = xhigh_bounds)
+
+using PSOGPU
+using CUDA
+
+CUDA.allowscalar(false)
+
+n_particles = 10_000
+
+gbest, particles = PSOGPU.init_particles(optprob, n_particles)
+
+gpu_data = cu(data)
+
+gpu_particles = cu(particles)
+
+@time gsol = PSOGPU.parameter_estim_ode!(prob,
+    gpu_particles,
+    gbest,
+    gpu_data,
+    lb = xlow_bounds, ub = xhigh_bounds; saveat = t, dt = 0.1)