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ModiaSim
Modia.jl
Julia2017–2024
ModiaSim / Modia.jl

Modia.jl

Modeling and simulation of multidomain engineering systems

7Distinct people
9Mentions
0Explicit recommenders
333GitHub stars
REPOSITORY AT A GLANCE

What to know before exploring

What it is

Modeling and simulation of multidomain engineering systems

GitHub

Where it is used

No documented product use has been verified yet.

What people are saying

No recommendation phrase was found in the indexed comments.

No classified warning is attached to this page. Read the discussions before treating silence as approval.

Also in these discussions

No other repository is repeatedly linked in the same discussions.

This week on Hacker News

Nov 7–Nov 14 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/14/2026, 3:56:07 PM UTC.

No direct repository links were found in the indexed HN discussions over the past seven days. Older discussions are available below.

Mentions over time

HN mentions by year9 dated mentions

Observed mentions: 2017-09-27 13:34:11 UTC to 2024-12-16 15:41:19 UTC.

0122017-01-01 00:00:00 UTC to 2018-01-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments20172018-01-01 00:00:00 UTC to 2019-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2019-01-01 00:00:00 UTC to 2020-01-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2020-01-01 00:00:00 UTC to 2021-01-01 00:00:00 UTC (exclusive): 2 mentions; 0 submissions, 2 comments20202021-01-01 00:00:00 UTC to 2022-01-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2022-01-01 00:00:00 UTC to 2023-01-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2023-01-01 00:00:00 UTC to 2024-01-01 00:00:00 UTC (exclusive): 2 mentions; 0 submissions, 2 comments2024-01-01 00:00:00 UTC to 2025-01-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2024

Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.

View mention counts and date ranges
UTC ranges include their start and exclude their end. Select a period to filter the source evidence.
PeriodStart (UTC)End (exclusive, UTC)MentionsSubmissionsComments
20172017-01-01 00:00:00 UTC2018-01-01 00:00:00 UTC101
20182018-01-01 00:00:00 UTC2019-01-01 00:00:00 UTC000
20192019-01-01 00:00:00 UTC2020-01-01 00:00:00 UTC101
20202020-01-01 00:00:00 UTC2021-01-01 00:00:00 UTC202
20212021-01-01 00:00:00 UTC2022-01-01 00:00:00 UTC101
20222022-01-01 00:00:00 UTC2023-01-01 00:00:00 UTC101
20232023-01-01 00:00:00 UTC2024-01-01 00:00:00 UTC202
20242024-01-01 00:00:00 UTC2025-01-01 00:00:00 UTC101

What Hacker News says

9 sources
If you intend to explore OpenModelica you may also like ModelingToolkit.jl: https://docs.sciml.ai/ModelingToolkit/dev/ There is also a project by Hilding Elmqvist, who worked for Dassault on Dymola (the leading commercial implementation of Modelica). His project is Modia.jl: https://github.com/ModiaSim/Modia.jl I can personally feel the Julia community settling on MTK, but Modia was ahead in the early stages of dynamic system simulation in Julia, and I believe MTK has drawn a lot of inspiration from each Modia and Modelica. Modia is a bit more ergonomic while also being the first to integrating things like 3D viewers and a complete multibody package by years, with Julia Computing only now catching up [1]. MTK has a better support for back-end solvers and holds a lot of promise to leapfrog Modia, especially since the release cadence for Mo
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In Modelica
For circuits a lot of them are represented by differential-algebraic equations (DAEs) and require modeling tools in order to handle the high differential index of the systems. This is the reason why they are typically handled via acausal modeling systems which can do index reduction. For Julia, this is the ModelingToolkit portion of the SciML ecosystem (https://docs.sciml.ai/ModelingToolkit/stable/), and some modeling tools like https://github.com/ModiaSim/Modia.jl and OpenModelica front-ends https://github.com/OpenModelica/OMJulia.jl.
In The Analog Thing: an open source, educational, low-cost modern analog computer
The creators of Modelica, Hilding Elmqvist and Martin Otter, work in Julia these days so there's been a boom to the development of acausal modeling tooling in Julia. They built https://github.com/ModiaSim/Modia.jl and then we collaborated with them on some ideas to build ModelingToolkit.jl (https://docs.sciml.ai/ModelingToolkit/dev/). The latest versions and experiments in OpenModelica are now targeting ModelingToolkit as a compilation backend, which gives a pathway for legacy Modelica code to be used with the MTK ecosystem. The OpenModelica community has always been a very small group in comparison to the Dymola compiler team and so the open part of Modelica never really caught on, but we hope to help them piggyback off of our compiler to get it there. We'll have a workshop on this collaboration in Linköping next February if you're inter
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ested in the details.
In The Analog Thing: an open source, educational, low-cost modern analog computer
The thing to watch in the space of Simulink/Modelica is https://github.com/SciML/ModelingToolkit.jl . It's an acausal modeling system similar to Modelica (though extended to things like SDEs, PDEs, and nonlinear optimization), and has a standard library (https://github.com/SciML/ModelingToolkitStandardLibrary.jl) similar to the MSL. There's still a lot to do, but it's pretty functional at this point. The two other projects to watch are FunctionalModels.jl (https://github.com/tshort/FunctionalModels.jl, which is the renamed Sims.jl), which is built using ModelingToolkit.jl and puts a more functional interface on it. Then there's Modia.jl (https://github.com/ModiaSim/Modia.jl) which had a complete rewrite not too long ago, and in its new form it's fairly similar to ModelingToolkit.jl and the differences are more in the details. For causal m
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odeling similar to Simulink, there's Causal.jl (https://github.com/zekeriyasari/Causal.jl) which is fairly feature-complete, though I think a lot of people these days are going towards acausal modeling instead so flipping Simulink -> acausal, and in that transition picking up Julia, is what I think is the most likely direction (and given MTK has gotten 40,000 downloads in the last year, I think there's good data backing it up). And quick mention to bring it back to the main thread here, the DataDrivenDiffEq symbolic regression API gives back Symbolics.jl/ModelingToolkit.jl objects, meaning that the learned equations can be put directly into the simulation tools or composed with other physical models. We're really trying to marry this process modeling and engineering world with these "newer" AI tools.
In ‘Machine Scientists’ Distill the Laws of Physics from Raw Data
Julia's meta-programming functionality is ideal for implementing such domain-specific languages (the popular JuMP suite is a flagship example in a different domain: https://jump.readthedocs.io/en/latest/). Several groups are working on systems inspired by Modelica, including: http://tshort.github.io/Sims.jl/latest/ and https://www.modelica.org/events/modelica2017/proceedings/htm... (https://github.com/ModiaSim/Modia.jl)
In A Comparison of Differential Equation Solver Suites

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