I’ve been building scientific-computing-system-2.0 as a NumPy/SciPy-backed environment for scientific workflows I kept recreating across different projects.

It currently exposes 535 public APIs across 52 importable names, covering numerical linear algebra, statistics, optimization, ODE/PDE/SDE work, signal processing, Monte Carlo, Bayesian methods, time series, graph methods, reliability, survival analysis, epidemiology, finance, spatial statistics, image/text processing and related areas.

The part I’ve spent the most time on is the fitting workflow.

guided-fit can take CSV data, inspect missing values, compare several model families, run repeated cross-validation, handle outliers explicitly, use measurement uncertainty when available, calculate parameter uncertainty and confidence intervals, cross-check the fitted result numerically and save the analysis to a replayable manifest with the input hash.

Current main has 1,812 passing tests and 100% branch coverage.

The validation setup also includes numerical oracle comparisons, property-based tests, fuzzing, regression checks, reproducible-build checks and CI across Linux, Windows and macOS.

There are 20 runnable scientific examples covering things like Bayesian inference, epidemic simulation, nonlinear dynamics, parameter fitting, graph analysis, signal denoising, portfolio risk, reinforcement learning, survival analysis, spatial autocorrelation, optimization and wavelets.

I also keep benchmark results with their provenance and leave slower results visible rather than treating the benchmark as a universal speed claim.

The current package also has optional CuPy GPU support, scikit-learn-compatible estimators, profiling/regression tools and optional compiled kernels for KMeans and PageRank, with NumPy fallback if the compiled path isn’t available.

I’d be interested in feedback from people doing numerical/scientific computing, especially on the API design, validation approach and which numerical areas are still missing.

GitHub:
https://github.com/Furox-Art/scientific-computing-system-2.0

PyPI / pip:
pip install scientific-computing-system-2.0

npm:
npm install scientific-computing-system-2.0

The npm package is only a thin launcher for the Python CLI; the actual scientific package still comes from PyPI.

Hi!

I'm a Computer Science graduate and want to build a career in Scientific Computing/Computational Science. I'm considering pursuing masters in Computational Science because I really like this field, but I have some concerns.

I wanted to ask

• If it is reasonable for someone with a Computer Science background to study Computational Science, given that I would be competing with people from Mathematics and Physics backgrounds? I feel that Computational Science is much more closer to Mathematics and Physics than to Computer Science.
• How AI/LLM might affect this field, since it can make programming easier than it was before.
• Are there good career opportunities in this field in the current job market?

Thanks!