From b9130e96db947062925ede83a7ecaac132e2d409 Mon Sep 17 00:00:00 2001 From: ArnoStrouwen Date: Sun, 23 Oct 2022 04:28:31 +0200 Subject: [PATCH] canonize docs --- README.md | 5 ++--- docs/src/tutorials/gpu_bayesian.md | 5 ++--- docs/src/tutorials/introduction.md | 4 ++-- 3 files changed, 6 insertions(+), 8 deletions(-) diff --git a/README.md b/README.md index 61c731e..928e454 100644 --- a/README.md +++ b/README.md @@ -1,11 +1,10 @@ # SciMLExpectations.jl: Expectated Values of Simulations and Uncertainty Quantification -[![Join the chat at https://gitter.im/JuliaDiffEq/Lobby](https://badges.gitter.im/JuliaDiffEq/Lobby.svg)](https://gitter.im/JuliaDiffEq/Lobby?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge) +[![Join the chat at https://julialang.zulipchat.com #sciml-bridged](https://img.shields.io/static/v1?label=Zulip&message=chat&color=9558b2&labelColor=389826)](https://julialang.zulipchat.com/#narrow/stream/279055-sciml-bridged) [![Global Docs](https://img.shields.io/badge/docs-SciML-blue.svg)](https://docs.sciml.ai/SciMLExpectations/stable/) +[![codecov](https://codecov.io/gh/SciML/SciMLExpectations.jl/branch/master/graph/badge.svg)](https://codecov.io/gh/SciML/SciMLExpectations.jl) [![Build Status](https://github.com/SciML/SciMLExpectations.jl/workflows/CI/badge.svg)](https://github.com/SciML/SciMLExpectations.jl/actions?query=workflow%3ACI) -[![Coverage Status](https://coveralls.io/repos/SciML/SciMLExpectations.jl/badge.svg?branch=master&service=github)](https://coveralls.io/github/JuliaDiffEq/SciMLExpectations.jl?branch=master) -[![codecov.io](http://codecov.io/github/JuliaDiffEq/SciMLExpectations.jl/coverage.svg?branch=master)](http://codecov.io/github/SciML/SciMLExpectations.jl?branch=master) [![ColPrac: Contributor's Guide on Collaborative Practices for Community Packages](https://img.shields.io/badge/ColPrac-Contributor's%20Guide-blueviolet)](https://github.com/SciML/ColPrac) [![SciML Code Style](https://img.shields.io/static/v1?label=code%20style&message=SciML&color=9558b2&labelColor=389826)](https://github.com/SciML/SciMLStyle) diff --git a/docs/src/tutorials/gpu_bayesian.md b/docs/src/tutorials/gpu_bayesian.md index a28c026..aa5f42b 100644 --- a/docs/src/tutorials/gpu_bayesian.md +++ b/docs/src/tutorials/gpu_bayesian.md @@ -130,8 +130,7 @@ This is the Monte Carlo approach and it converges to the correct answer by However, the [Koopman expectation](https://arxiv.org/abs/2008.08737) can converge with much fewer points, allowing the use of higher order quadrature methods to -converge exponentially faster in many cases. To use the Koopman expectation -functionality provided by [SciMLExpectations.jl](https://github.com/SciML/SciMLExpectations.jl), +converge exponentially faster in many cases. To use the Koopman expectation, we first need to define our observable function `g`. This function designates the thing about the solution we wish to calculate the expectation of. Thus for our question "what is the expected value of `x`at time `t=10`?", we would simply use: @@ -172,7 +171,7 @@ Are we done? No, we need to add some GPUs! As mentioned earlier, probability calculations can take quite a bit of ODE solves, so let's parallelize across the parameters. [DiffEqGPU.jl](https://github.com/SciML/DiffEqGPU.jl) allows you to GPU-parallelize across parameters by using the -[Ensemble interface](https://diffeq.sciml.ai/stable/features/ensemble/). Note that +[Ensemble interface](https://docs.sciml.ai/DiffEqDocs/stable/features/ensemble/). Note that you do not have to do any of the heavy lifting: all of the conversion to GPU kernels is done automaticaly by simply specifying `EnsembleGPUArray` as the ensembling method. For example: diff --git a/docs/src/tutorials/introduction.md b/docs/src/tutorials/introduction.md index ed5953d..3cc1878 100644 --- a/docs/src/tutorials/introduction.md +++ b/docs/src/tutorials/introduction.md @@ -279,12 +279,12 @@ centralmoment(5, g, prob, u0_dist, p, Koopman(), Tsit5(), ``` ## Batch-Mode -It is also possible to solve the various simulations in parallel by using the `batch` kwarg and a batch-mode supported quadrature algorithm via the `quadalg` kwarg. To view the list of batch compatible quadrature algorithms, refer to [Quadrature.jl](https://github.com/SciML/Quadrature.jl). Note: Batch-mode operation is built on top of DifferentialEquation.jl's `EnsembleProblem`. See the [EnsembleProblem documentation](https://diffeq.sciml.ai/stable/features/ensemble/) for additional options. +It is also possible to solve the various simulations in parallel by using the `batch` kwarg and a batch-mode supported quadrature algorithm via the `quadalg` kwarg. To view the list of batch compatible quadrature algorithms, refer to [Integrals.jl](https://docs.sciml.ai/Integrals/stable/). Note: Batch-mode operation is built on top of DifferentialEquation.jl's `EnsembleProblem`. See the [EnsembleProblem documentation](https://docs.sciml.ai/DiffEqDocs/stable/features/ensemble/) for additional options. The default quadtrature algorithm used by `expectation()` does not support batch-mode evaluation. So, we first load dependencies for additional quadrature algorithms ```julia -using Quadrature, Cuba +using Integrals, Cuba ``` We then solve our expectation as before using a `batch=10` multi-thread parallelization via `EnsembleThreads()` of Cuba's SUAVE algorithm. However, in this case we introduce additional uncertainty in the model parameter.