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Aug 4, 2025
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Small Grant Request+Completion: DAE Problem Benchmarks #168

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30 changes: 29 additions & 1 deletion small_grants.md
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Expand Up @@ -232,7 +232,7 @@ solvers in a standard SciMLBenchmarks benchmark build.

## DAE Problem Benchmarks (\$100 / Benchmark)

**In Progress**: Claimed by Jayant Pranjal for the time period of 26th June 2025 - 26th July 2025.
**In Progress**: Claimed by Jayant Pranjal for the time period of 24th July 2025 - 24th Aug 2025.

New benchmarks for differential-algebraic equation (DAE) systems would greatly improve our
ability to better tune solvers across problems. However, we are currently lacking in the
Expand Down Expand Up @@ -334,6 +334,34 @@ which SciML will help administer through the small grants program.

These are the previous SciML small grants projects which have successfully concluded and paid out.

## DAE Problem Benchmarks (\$100 / Benchmark)

Completed by **Jayant Pranjal**
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@ChrisRackauckas ChrisRackauckas Jul 26, 2025

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This one isn't merged yet.

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@ChrisRackauckas ChrisRackauckas Aug 2, 2025

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Merged now 🎉 so this is done

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@jayantpranjal0 jayantpranjal0 Aug 3, 2025

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Thanks😊


**Benchmarks added:** NAND Gate Problem benchmark

New benchmarks for differential-algebraic equation (DAE) systems would greatly improve our
ability to better tune solvers across problems. However, we are currently lacking in the
number of such benchmarks that exist. The goal would be to add standard benchmarks from
[this issue](https://github.com/SciML/SciMLBenchmarks.jl/issues/359) to the SciMLBenchmarks
system so that they can be performance tracked over time.

**Information to Get Started**: [Contributing Section of the SciMLBenchmarks README](https://github.com/SciML/SciMLBenchmarks.jl?tab=readme-ov-file#contributing)
describes how to contribute to the benchmarks. The benchmark results are
generated using the benchmark server. The [transition amplifier benchmark](https://github.com/SciML/SciMLBenchmarks.jl/pull/372)
and [slider crank benchmark](https://github.com/SciML/SciMLBenchmarks.jl/pull/373) were old
PRs to add a few of the problems. These could be used as starting points to solve two problems.
One would likely need to modify the structural simplification to turn dummy derivative off
as well, that can be discussed with Chris in the PR review.

**Related Issues**: [https://github.com/SciML/OrdinaryDiffEq.jl/issues/2177](https://github.com/SciML/OrdinaryDiffEq.jl/issues/2177)

**Success Criteria**: New benchmarks with the DAE systems.

**Recommended Skills**: Prior knowledge in modeling with differential-algebraic equations
would be helpful for debugging.

**Reviewers**: Chris Rackauckas

## Wrap PyCMA into the Optimization.jl Interface (\$100)

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