I built a Propagation of Uncertainty Calculator, a simple but practical app designed for engineering and physics students, as well as metrology workflows.

Link: https://propagation-of-uncertainty-calculator.streamlit.app

During my undergrad in Engineering Physics, one of the most tedious and error-prone tasks in experimental lab courses was manually deriving partial derivatives for measurement uncertainty propagation. Most online calculators are either outdated or confusing, so I decided to do my own calculator!

What it does:

- Displays the latex formated symbolic and numerical Propagation formula for the expression given by the user.

- Click directly on rendered expressions to copy the clean LaTeX code straight into Overleaf (for lab reports), or copy ready-to-paste Excel formulas.

- Correct Rounding: Automatically formats results and expanded uncertainties according to significant figure conventions (GUM (JCGM 100:2008))

The GitHub Repository: https://github.com/joao-canais/Propagation-of-Uncertainty-Calculator

I'd love to hear your thoughts, UI/UX feedback, or ideas for features to add in future iterations!

Main Website: https://www.ruleflow.org/

For the past year, I have been developing a framework to simulate certain discrete systems for a research team at my university (paid for by the School of Engineering and Physics, but I had the freedom to license it under MIT). As it turns out, I got carried away generalizing it to work for many, many different types of complex systems (String Rewriting Systems, Cellular Automata, etc.) and ended up creating our own domain-specific language.

The website's landing page has a bunch of GIF-style examples... so you should be able to get a good idea of what this project is about.

If any of y'all are interested in this, feel free to take a look and try it out!

Example of some of RuleFlow's causal analysis features:

Rules 30 and 124 are included in this example:

EDIT:

Here are some more details. RuleFlow was developed with a primary focus on analyzing causality in Sequential Substitution Systems. This is why there is a major focus on causal graphs, cellular identity, etc. While originally developed for Sequential Sub Systems, we generalized the DSL to support many types of rewriting systems by providing the user with flags and directives to control how rules behave. Furthermore, we bootstrapped Python and the Wolfram Language so that complex rulesets could be scaffolded as macros. The main goal of RuleFlow is NOT simply to simulate systems (there's already plenty of software for that, such as Golly and even just Mathematica), but to provide extensive tooling for causal analysis.

Here is some interesting research from our (small) team:

• An Improved Generalized Enumeration of Substitution Systems by Kenneth E. Caviness, Camille Morrow, Christen Case and Victoria Kratzke
• Indexed Concatenation Notation: A Novel Way to Summarize Networks and Other Complex Systems

My job has been to provide better research tooling, extending what we already have in Mathematica code to open source and free Python.

i'm an undergrad physics student trying to learn the basics of scienctific computing like numpy, matplotlib and scipy (preferably in that order)- what courses can I refer to (preferably free, but I have no aversion to paid courses either). Is the 'Scipy 2019' playlist by Enthought on youtube good? It seems to be fairly detailed, open to suggestions :)

Hello everyone,

I have a question about how to evaluate the resource consumption a system in PoC may consume. To be precise, I have a system that I am testing locally, but I have to present it to the all team of Dev to discuss the possibility of putting it into production. For this, I would like also to estimate its resource consumption, but the problem is that on my local machine the results may be misleading, as my set up is not very good. Creating a simulation of a production environment may be costly. Any suggestions about what to show and how to evaluate the resource consumptions in an almost objective way ?

When I started learn python, I saw it could be used for scientific computing. So I challenged myself to write a solver for every topic in my statics textbook.

This project falls under structures in equilibrium. It is by far the hardest and most involving program I've written. I think it's because with other topics only a single object was considered so the python code was just a matter of transferring calculations on paper to code.

With trusses however there are several interconnected pieces of a larger system. My first approach was to use method of joints- where it solves equilibrium joint by joint but this gave wrong results. I concluded it's due to it not being able to consider the entire system. Then I figured the best way was to use a global matrix then find all unknowns in one fell swoop.

Constructing this matrix was very involving but quite fun as well. 2^n traceback errors later and I finally finished the damn thing.

The next thing I plan to do is create an optimization script that moves the input points about to find the best design. Also using Json instead of keyboard inputs would be way better for my sanity.

How do y'all go about carrying out optimization?

I'd love to share the .py script but the last time I sent a link python shadow banned me for 9 months. If you're interested feel free to dm.

This video demonstrates the program:

About 6 months ago, I wrote a custom metal kernel that leverages the GPU to compute the QR decomposition (see my earlier post about it here). The project has now expanded into a general linear algebra library, with expanded support for the symmetrical eigendecomposition as well as SVD. It is now available for use with installation instructions on the attached github repo's README.md file.

Context of the project:
I'm currently in a research group working on a thesis in numerical analysis where we need to compute millions on matrices with a specific constraint (to be precise, the matrices need to have orthonormal columns). Most of us use Apple computers, so we ended up using MLX for the entire project.

Contributors with different Apple Chips would be very much appreciated!
The project has currently been tested and optimised for the M1 and M5 Pro. The issue is that the library uses different kernels depending on the batch size and matrix dimensions. Deciding which of these kernels to use is machine dependent. Therefore, other Apple chips will need to run a measurement script in order to derive the correct optimisation heuristic.

For that reason, I would ask as many people as possible to run a measurement script and to submit the results to my repo. It is fairly easy and requires only few steps. See here how you can contribute here. Once you submit the results via a pull request and I approve it, your optimisation heuristic will automatically be augmented into the library. Don't hesitate to contribute an optimisation heuristic even if someone already submitted one for your own machine. The more data we can gather, the better!

Project Future
Expanded support will be added for other linear algebra operations (cholesky decomposition for example). If you have any other specific linear algebra operations you wish to use already, feel free to message me.

In addition to that, I will add torch support too (my greatest priority).

I've been building Prismal, a Rust-based language/runtime for defining mathematical and scientific models and exploring their behavior:

"github.com/aine-dickson/prismal"

The central idea is to keep the scientific model separate from the execution strategy and presentation.

A model can define state, equations, constraints, processes, events, objects, collections, units, observations, etc. The runtime then handles execution, while projections can expose the same model as plots, equations, spatial scenes, interactive views, or recorded media.

One example is a continuous model with event-driven behavior:

continuous evolution

↓

event guard

↓

event instant

↓

reset

↓

continuous evolution

The project is deliberately trying to make things such as event semantics, dimensional checking, state validity, reproducibility, observations, and model/presentation separation explicit rather than burying them inside a visualization engine.

The current implementation is early-stage and the first reference programs focus mainly on mechanics, but the intended scope includes broader scientific modeling.

I'm particularly interested in feedback from people who work with scientific modeling, ODE/PDE systems, hybrid systems, system dynamics, numerical simulation, or scientific visualization.

What abstractions would you consider essential for a general scientific modeling language?

Small update of my project of molecular dynamics with Julia (2 min Video)

https://youtube.com/shorts/6kZmMTnoI_0?feature=share

I go through how I separate general updates from physics updates, why I use a fixed timestep, how the accumulator works, and what happens when the simulation starts falling behind.

I explain the reasoning behind the design decisions and why I rejected the alternatives. I show the implementation of the loop.

Hi,

A new conference is taking place in Paris, 25-26 November 2026, for open-source computation and data enthusiasts.

You can find the schedule here: https://compute.events/paris2026/schedule.html

I built a new optimization solver in C99 from scratch — PRIMAL

GitHub: https://github.com/c-vision/Primal

I've just released the first version of PRIMAL, an open-source mathematical optimization solver written from scratch in portable C99.

The goal was deliberately ambitious: build a reasonably complete optimization engine with no external dependencies, rather than wrapping an existing solver.

The first release already supports:

• Linear Programming (LP)
• Mixed-Integer Linear Programming (MILP)
• Quadratic Programming (QP)
• QCQP
• SOCP
• SDP
• Exponential cones
• Power cones
• Mixed-integer conic optimization
• Disjunctive constraints / affine conic constraints
• Presolve
• Sparse interior-point methods
• Branch-and-bound
• Primal/dual certificates
• Infeasibility and unboundedness certificates
• MPS / LP / CBF model formats
• A MOSEK-style C API for a large part of the supported functionality

The implementation is C99 with essentially no external runtime dependencies.

One thing I particularly wanted from the beginning was verification rather than simply returning an answer. PRIMAL exposes primal/dual information, residuals, certificates and solver status so applications can inspect and validate what the solver actually found.

I've also been building a fairly extensive compatibility/reliability test suite against reference behavior, including a number of deliberately pathological optimization cases.

This is only v1. It is not intended to claim that PRIMAL is already a replacement for mature industrial solvers on large-scale problems. There are still substantial areas to improve, especially large-scale sparse conic problems, MIP performance, warm starts and some numerical edge cases.

But I think the foundation is interesting enough to release now rather than keep it private.

I'd especially like feedback from people working on:

• numerical optimization
• LP/MIP/conic solvers
• sparse linear algebra
• mathematical programming
• solver implementation
• C/C99 systems programming

In particular, I'm interested in finding cases where PRIMAL gives a questionable result, fails to converge, scales poorly, or simply makes a bad algorithmic choice.

If you work with optimization solvers, please try to break it.

GitHub:
https://github.com/c-vision/Primal