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Fix showcase doc breakages: SRIW1 import + 3-arg prob_func #337

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Merged
ChrisRackauckas merged 1 commit into from
May 10, 2026
Merged

Fix showcase doc breakages: SRIW1 import + 3-arg prob_func #337

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2 changes: 2 additions & 0 deletions docs/Project.toml
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Original file line number Diff line number Diff line change
Expand Up @@ -52,6 +52,7 @@ StableRNGs = "860ef19b-820b-49d6-a774-d7a799459cd3"
StaticArrays = "90137ffa-7385-5640-81b9-e52037218182"
Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
StatsPlots = "f3b207a7-027a-5e70-b257-86293d7955fd"
StochasticDiffEq = "789caeaf-c7a9-5a7d-9973-96adeb23e2a0"
SymbolicIndexingInterface = "2efcf032-c050-4f8e-a9bb-153293bab1f5"
SymbolicRegression = "8254be44-1295-4e6a-a16d-46603ac705cb"
Symbolics = "0c5d862f-8b57-4792-8d23-62f2024744c7"
Expand Down Expand Up @@ -107,6 +108,7 @@ StableRNGs = "1"
StaticArrays = "1"
Statistics = "1"
StatsPlots = "0.15"
StochasticDiffEq = "6"
SymbolicIndexingInterface = "0.3"
SymbolicRegression = "1"
Symbolics = "6, 7"
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5 changes: 3 additions & 2 deletions docs/src/showcase/gpu_spde.md
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Expand Up @@ -474,8 +474,9 @@ end
Now we just define and solve the system of SDEs:

```@example spde
prob = DE.SDEProblem(f, g, u0, (0.0, 100.0))
@time sol = DE.solve(prob, DE.SRIW1());
import StochasticDiffEq as SDE
prob = SDE.SDEProblem(f, g, u0, (0.0, 100.0))
@time sol = SDE.solve(prob, SDE.SRIW1());
```

```@example spde
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2 changes: 1 addition & 1 deletion docs/src/showcase/massively_parallel_gpu.md
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Expand Up @@ -83,7 +83,7 @@ Now, from this problem, we build an `EnsembleProblem` as per the DifferentialEqu
specification. A `prob_func` jiggles the parameters and we solve 10_000 trajectories:

```@example diffeqgpu
prob_func = (prob, ctx) -> ODE.remake(prob, p = (StaticArrays.@SVector rand(Float32, 3)) .* p)
prob_func = (prob, i, repeat) -> ODE.remake(prob, p = (StaticArrays.@SVector rand(Float32, 3)) .* p)
monteprob = DiffEqGPU.EnsembleProblem(prob, prob_func = prob_func, safetycopy = false)
sol = ODE.solve(monteprob, ODE.Tsit5(), DiffEqGPU.EnsembleThreads(), trajectories = 10_000, saveat = 1.0f0)
```
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2 changes: 1 addition & 1 deletion docs/src/showcase/optimization_under_uncertainty.md
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Expand Up @@ -69,7 +69,7 @@ import Distributions
cor_dist = Distributions.truncated(Distributions.Normal(0.9, 0.02), 0.9 - 3 * 0.02, 1.0)
trajectories = 100

prob_func(prob, ctx) = DE.remake(prob, p = [p[1], rand(cor_dist)])
prob_func(prob, i, repeat) = DE.remake(prob, p = [p[1], rand(cor_dist)])
ensemble_prob = DE.EnsembleProblem(prob, prob_func = prob_func)
ensemblesol = DE.solve(ensemble_prob, DE.Tsit5(), DE.EnsembleThreads(), trajectories = trajectories,
callback = cbs)
Expand Down