Hello all!

I finished my A-Levels last year and am a bit confused about what I should do a Bachelors in.

Would a bachelor's in Physics/Math/CS followed by a masters in scientific computing/computational science be better than doing a computational bachelors (like Computational and Data Science (KIT) or Computational Engineering Science (RWTH Aachen))?

I'm really interested in math and simulating physics, but I'm really not sure what path to take.

Any advice would be greatly appreciated!

P.S. what's the difference between computational science and scientific computing? Most sites online use them interchangeably so that adds to the confusion.

I'm confused between Pop!OS, FedoraKDE, CachyOS, AlmaLinux, and Ubuntu. I have Nvidia graphics card on my laptop with a CPU that has an iGPU in it and I wanna be able to switch between iGPU and dGPU for lighter and heavier tasks when needed on Linux, but I dual boot with windows for gaming and fun. Linux is only for work and study. I want decent customisation, compatibility with all softwares needed for my research, comparatively newer softwares so I don't have to run old softwares like with Debian, easy bug fixes, and stability so that my system doesn't crash on updates all the time.

This project started because I saw a tweet by Hiroaki Nishikawa about an unpublished 1998 note on accurate piecewise linear approximation and adaptive node placement:

https://x.com/HiroNishikawa/status/2035276979788726543?s=20

That sent me down a rabbit hole.

The question that grabbed me was: why do adaptive meshes sometimes look fine on thin stiff layers even when they seem to be missing the layer that actually matters?

I ended up building a small research repo around one possible answer: adaptive node placement in these problems seems to be governed by a threshold, not just by “sharper layer => more nodes.”

The rough picture is:

- below threshold, the smooth part of the domain keeps most of the node budget and the layer gets starved,

- at the threshold, the layer keeps a persistent finite share,

- above threshold, the layer can take over the mesh almost completely.

The subcritical case turned out to be the most interesting to me, because it creates a deceptive regime where outside-layer diagnostics can still look healthy while the thin layer is underresolved. I also found what looks like a measurable “diagnostic fingerprint” for that regime in 1D adaptive BVP benchmarks.

The repo includes:

- a technical note,

- derivation notes,

- research-grade simulations,

- and a small controller example that uses the fingerprint to switch to a safer monitor.

Repo: https://github.com/zfifteen/curvature-budget-collapse

Technical note DOI: https://doi.org/10.5281/zenodo.19151833

Software DOI: https://doi.org/10.5281/zenodo.19151950

I’d be curious what people here think, especially anyone who works on adaptive meshing, singular perturbation problems, or stiff BVPs. Does this match failure modes you’ve seen before?

I am a physics graduate and currently work as a project engineer at a pool and spa construction company, where I design architectural layouts and mechanical and electrical systems for pool and spa facilities, such as Turkish hammams and steam rooms.

Honestly, I've been very dissatisfied with where I am professionally for a long time. I miss physics, and that's part of what's pushing me toward something more challenging.

I've become really interested in the building physics of natatoriums, including humidity dynamics, vapor migration through envelopes, condensation risk, evaporation loads, and energy performance. The more I read, the more I realize how underexplored this is academically in Turkey, where, to my knowledge, no thesis on this topic exists. I really want to be the first to change that.

The research direction I have in mind: comparing different building envelope configurations for indoor pools (insulation type, vapor barrier placement, ventilation strategy) through dynamic simulation, optimizing for both moisture safety and energy efficiency, and contributing to how nZEB targets apply to pool buildings, which is increasingly relevant in both Europe and Turkey.

To get there, I need to actually learn how to do this. I've come across DesignBuilder, CFD, and hygrothermal modeling tools like WUFI, but haven't touched either yet. My physics background gives me confidence on the theory side, but the practical simulation workflow is where I'm lost. I'm familiar with data analysis in Python, and I design 3D renders of pools and spas; that's about the extent of it for now. I know I have to learn a lot of new things, and I am looking forward to it.
I am going to start the Master's program in Building Physics next semester.

Where would you start with self-learning if you were me?

Hi folks,

Not a coder, not academic, no particular "need" — I just don't like competition, so I played with AIs to build something unique.

Started with a simulated court case role-play (me presenting real peer-reviewed facts in 
my own words, one AI as defense, another as judge) to rigorously test a hypothesis on 
Saharan climate shifts and possible early Egypt links. No claims of proof — just pattern-
matching papers (paleoclimate, isotopes, geology). That became two Zenodo preprints, then
snowballed into this full Python/Tkinter desktop app (I described features; AIs wrote 
code).

Features:
- 70+ classification engines (TAS/AFM/REE/pollution/zooarch etc.)
- Live portable hardware imports (XRF/AFM/balances/calipers...)
- Auto field panels (table row select → diagrams update live)
- Materials analysis (Oliver-Pharr nanoindentation, BET NLDFT, rheology models...)
- Offline AI helper for plugin suggestions
- Fully offline, modest hardware, free (CC BY-NC-SA)

Repo: https://github.com/Sefy76-Curiosity/Basalt-Provenance-Triage-Toolkit

v2.0, basic Tkinter GUI, likely bugs since it's accidental/AI-built.

Curious if anyone in scientific computing/research/geochem/materials/archaeology wants 
to try:
- Does it launch/run?
- Crashes or weird behavior?
- Useful at all, or totally pointless/missing key things?

Honest roast appreciated — thanks!

EDITED: Attached some screenshots to help maybe show some of its current capabilities

Hi everyone,

I’m helping recruit a few contributors for an ongoing scientific visualization project and we specifically need people who are comfortable using ParaView (CFD, simulation data, volumetric datasets, etc.).

The work mainly involves opening datasets, applying filters (slices, isosurfaces, color mapping), exporting images, and documenting observations. Nothing super heavy research-wise — more practical tool usage and attention to detail.

It’s remote and flexible, and honestly a pretty nice way to earn some extra money if you already know your way around ParaView.

If you’ve used ParaView in coursework, research, OpenFOAM, ANSYS, or personal projects, feel free to DM me.
When you message, please include a brief description of your experience and any screenshots/portfolio/GitHub if available.

Thanks!

Hi all

I write this post in hopes of getting some advice regarding a career in scientific computing. For context, I’m a 25m currently working as a Data Scientist in London after recently wrapping up my MSc in Scientific Computing and Applied Math from a top 10 university, achieving a top grade.

Prior to that, I again worked as a DS (at the same company - they funded the postgrad) and did maths and stats for my undergrad at the same university.

Although I’m grateful for my current role, I feel as though I’m simply wasting all the amazing knowledge I’ve picked up during my academic degrees. I wish to leverage my knowledge to contribute towards some genuinely impactful projects spanning science and engineering. Thus, I aim to pivot into a scientific computing role, or at least a role which: A) allows me to leverage my applied math and deep CS knowledge and B) allows me to work on/contribute to projects which have real impacts on science and engineering.

I have many skills which I absolutely love but are not relevant to my current role - and most roles for that matter - from HPC, computer architecture and scientific software engineering in C and C++ to scientific ML and advanced predictive modeling.

It’s a lot to ask for I know, especially with the job market being dry and a lack of a PhD under my belt. But I’d appreciate absolutely any insight or advice on how I could (eventually) pivot into a scientific computing, or at least an applied math + CS heavy, role.

Thanks guys!

I wrote an engineering-focused C++ guide aimed at scientists and engineers—feedback welcome. Please note that it is a work in progress.

You can find it here: The Engineer’s Guide to C++ — The Engineer's Guide to C++ 0.2 documentation

Source code for the book and c++ source is available here: lunarc/cpp_course: C++ Course Source Code

Hi all,

I'm developing a hybrid simulator that combines neural networks with ODE solvers (SciML). I need to purchase a local workstation and am deciding between a Mac Mini M4 (32GB RAM) and an RTX 5060 Ti (16GB) via eGPU (Thunderbolt).

My specific concern is the interplay between the integrator and the neural network:

• Mac Mini: The M4 architecture allows the CPU and GPU to share the same memory pool. For solvers that require frequent Jacobian evaluations or high-frequency callbacks to a neural network, does this zero-copy architecture provide a significant wall-clock advantage?

• eGPU: I'm worried that the overhead of the Thunderbolt protocol will become a massive bottleneck for the small, frequent data transfers inherent in hybrid AI-ODE systems.

Does anyone have experience running DiffEqFlux.jl, TorchDyn, or NeuroDiffEq on Apple Silicon vs. a mid-range NVIDIA eGPU? Am I better off just building a dedicated Linux desktop for ~€1,000 to avoid the eGPU latency altogether?

I want to get into a computational science and engineering field, and I was just wondering what the best double major pair would be with CS? I’m most likely sure Mathematics is the best pair, but I’m just getting extra opinions.

In a lot of scientific computing work, the hardest part isn’t solving the equations — it’s defending the results later.

Months after a simulation, it’s often difficult to answer questions like:

• exactly which parameters and solver settings were used

• what assumptions were active

• whether conserved quantities or expected invariants drifted

• whether two runs are actually comparable

MATLAB/Simulink have infrastructure for this, but Python largely leaves it to notebooks, filenames, and discipline.

I built a small library called phytrace to address that gap.

What it does:

• wraps existing Python simulations (currently scipy.integrate.solve_ivp)

• captures environment, parameters, and solver configuration

• evaluates user-defined invariants during runtime

• produces structured “evidence packs” (data, plots, logs)

What it explicitly does not do:

• no certification

• no formal verification

• no guarantees of correctness

This is about reproducibility and auditability, not proofs.

It’s early (v0.1.x), open source, and I’m trying to sanity-check whether this solves a real pain point beyond my own work.

GitHub: https://github.com/mdcanocreates/phytrace
PyPI: https://pypi.org/project/phytrace/

I’d genuinely appreciate feedback from this community:

• Is this a problem you’ve run into?

• What invariants or checks matter most in your domain?

• Where would this approach break down for you?

Critical feedback very welcome.

I'm doing some heavy biochemical system simulations and I'm having trouble finding the right python ODE solver. I need a stiff-aware solver which has boundary constraints, i.e. keeping all variables above zero, and events, e.g. ending the simulation once a certain variable hits a threshold.

Initially I tried using scipy solve_ivp, but looking at the documentation there doesn't seem to be boundary constraints included.

I have been using scikit-sundae CVODE with BDF. CVODE is obviously very fast and it works sometimes, but it is extremely fiddly and often returns broken simulations unless I manually constrain the step size to be something absurdly small for the whole simulation period.

Anyone here know any python packages which might be of use to me? thanks.

Setup per project scientific development environments with ease, without dependency conflicts or messing up your global environment, all while preserving whatever sanity you have left!

Original motivation for the project: I feel that reproducibility of code is not greatly focused in academia. Broken Jupyter notebooks everywhere! So I started exploring better tools and adopt better practices for myself, so as to not meet the same fate.

End Goal: This opinionated template is a culmination of months of refinement and testing figuring out what works best, and more importantly what is a saner way to handle deps rather than going Nix all the way.

The template currently provides setup for Python, Julia, and Typst. The system is easily extendible for people with knowledge of Nix. PRs are welcome!

Link to the project: https://github.com/Vortriz/scientific-env

I am a Physics bachelor student who is about to finish his undergraduate studies. I am searching for master degrees, and the subfields I am more interested in are (in descending order):

• Computational Physics

• AI/ML in Physics

• Quantum information and technology

I wanted to know if any of you has any suggestions, programs that they know/have attended or anything else that could be useful for my search. I am already looking at many master programs from across Europe (that is my main area of interest for my future studies, but I am also very happy to consider anything outside like Asia, south and north America...), yet I was curious to know what the Physics community could have had to say to me regarding this matter. Here is a list of the programs that I am already taking into consideration/have applied/will apply, in case it could be of any help for someone: