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Improvements based on review #328

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Sep 24, 2024
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Improvements based on review #328

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ec31030
README: more explanation for related-package trade-offs
nathanaelbosch Sep 23, 2024
ad3faa6
Update and correct second-order-ODE docs
nathanaelbosch Sep 23, 2024
208d2ee
Update the related-package section in `docs/src/index.md`
nathanaelbosch Sep 24, 2024
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4 changes: 2 additions & 2 deletions README.md
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Expand Up @@ -77,5 +77,5 @@

## Related packages

- [probdiffeq](https://pnkraemer.github.io/probdiffeq/): Fast and feature-rich filtering-based probabilistic ODE solvers in JAX.
- [ProbNum](https://probnum.readthedocs.io/en/latest/): Probabilistic numerics in Python. It has not only probabilistic ODE solvers, but also probabilistic linear solvers, Bayesian quadrature, and many filtering and smoothing implementations.
- [ProbDiffEq](https://pnkraemer.github.io/probdiffeq/) is similar in scope to ProbNumDiffEq.jl and it provides fast and feature-rich probabilistic ODE solvers but is implemented in Python and built on JAX.

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[vale] reported by reviewdog 🐶
[Vale.Terms] Use 'probdiffeq' instead of 'ProbDiffEq'.

Raw Output:
{"message": "[Vale.Terms] Use 'probdiffeq' instead of 'ProbDiffEq'.", "location": {"path": "README.md", "range": {"start": {"line": 80, "column": 4}}}, "severity": "ERROR"}
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- [ProbNum](https://probnum.readthedocs.io/en/latest/) implements a wide range of probabilistic numerical methods, not only for ODEs but also for linear algebra, quadrature, and filtering/smoothing. It is implemented in Python and NumPy, and it focuses more on breadth and didactic purposes than on performance.
4 changes: 2 additions & 2 deletions docs/src/index.md
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Expand Up @@ -40,5 +40,5 @@

## Related packages

- [probdiffeq](https://pnkraemer.github.io/probdiffeq/): Fast and feature-rich filtering-based probabilistic ODE solvers in JAX.
- [ProbNum](https://probnum.readthedocs.io/en/latest/): Probabilistic numerics in Python. It has not only probabilistic ODE solvers, but also probabilistic linear solvers, Bayesian quadrature, and many filtering and smoothing implementations.
- [ProbDiffEq](https://pnkraemer.github.io/probdiffeq/) is similar in scope to ProbNumDiffEq.jl and it provides fast and feature-rich probabilistic ODE solvers but is implemented in Python and built on JAX.

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[vale] reported by reviewdog 🐶
[Vale.Terms] Use 'probdiffeq' instead of 'ProbDiffEq'.

Raw Output:
{"message": "[Vale.Terms] Use 'probdiffeq' instead of 'ProbDiffEq'.", "location": {"path": "docs/src/index.md", "range": {"start": {"line": 43, "column": 4}}}, "severity": "ERROR"}
- [ProbNum](https://probnum.readthedocs.io/en/latest/) implements a wide range of probabilistic numerical methods, not only for ODEs but also for linear algebra, quadrature, and filtering/smoothing. It is implemented in Python and NumPy, and it focuses more on breadth and didactic purposes than on performance.
6 changes: 3 additions & 3 deletions docs/src/tutorials/dynamical_odes.md
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Expand Up @@ -92,10 +92,10 @@ As a result, we can use the `EK1` both for first and second order ODEs, but it a
julia> using BenchmarkTools

julia> @btime solve(prob, EK1(order=3), adaptive=false, dt=1e-2);
766.312 ms (400362 allocations: 173.38 MiB)
317.336 ms (140561 allocations: 140.41 MiB)

julia> @btime solve(prob2, EK1(order=4), adaptive=false, dt=1e-2);
388.301 ms (510676 allocations: 102.78 MiB)
julia> @btime solve(prob2, EK1(order=3), adaptive=false, dt=1e-2);
136.654 ms (180647 allocations: 47.95 MiB)
```

## Energy preservation
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