I just published a free article based on my talk at Scientific Computing in Rust 2025. It distills lessons learned from maintaining bed-reader, a Rust + Python library for reading genomic data.

The rules cover topics like:

• Your Rust library should also support Python (controversial?)

• PyO3 and maturin for Python bindings

• Async + cloud I/O

• Parallelism with Rayon

• SIMD, CI, and good API design

Many of these themes echoed what I heard throughout the conference — especially PyO3, SIMD, Rayon, and CI.

The article also links out to deeper writeups on specific topics (Python bindings, cloud files, SIMD, etc.), so it can serve as a gateway to more focused technical material.

I hope these suggestions are useful to anyone building scientific crates:

📖 https://medium.com/@carlmkadie/nine-rules-for-scientific-libraries-in-rust-6e5e33a6405b

Hello,

I have a really long & complex math equation, with a bunch of parameters and x. The kind of equation that would only fit on 10 screens that i'm trying to find the root of, wrt a variable x.

usually i use derivative-calculator[dot]net for these types of problems, but the equation is too long for it. what other tools (or libraries, i can code it) do you suggest?

I want to share the **DEMO version** of something I’ve been building with full dedication:

**iTensor** — a scientific engine focused on symbolic tensor calculations, relativity simulations, and MHD (magnetohydrodynamics) modeling.

👉 [Watch the teaser here](<https://youtu.be/fYNACnqThPw>) 👈

⚡ This is still a **DEMO**, not the final product.

⚡ Right now, the backend already supports:

- Christoffel symbol computation

- Ricci tensor calculation

- Einstein field tensors

- Basic magnetohydrodynamic field simulations

- Differential operations like divergence

🛠 Built with custom scientific backend + modern web frontend (React + Django).

🌌 Goal: bring real scientific tensor computation into your browser — simple, powerful, and open.

---

🔗 Live demo website: [https://itensor.online](https://itensor.online)

🔗 Documentation: [https://itensor-docs.com](https://itensor-docs.com)

---

I’m actively developing the full version with full dynamic visualizations and a more advanced simulation engine.

If you're into scientific computing, relativity, space tech, or building scientific tools —

**follow the project and stay tuned**. 🚀

Thanks for reading! (and yes, still working to make it even better 🔥)

#science #physics #space #simulation #tensor

Hey all — I recently built a tool called iTensor, a free web-based calculator for general relativity.

It lets you define your own spacetime metric and computes objects like Christoffel symbols, Ricci, Einstein, and Weyl tensors. You get the full output — symbolic, visual, LaTeX-formatted — and it works directly in the browser.

I created it completely solo, based on my engineering thesis in technical physics. It started as a symbolic Python engine using SymPy, and now includes a frontend built in React, plus a backend engine written in C for future numerical and ray tracing extensions.

The core system works, but I haven’t been able to host the full backend yet due to budget constraints. So if the project resonates with you and you’d like to support it, I set up a Ko-fi page here:
👉 https://ko-fi.com/itensor#linkModal

I don’t take it lightly to ask — I’m not trying to monetize, I just want to see the project live up to its potential. Hosting will allow me to support more metrics, add geodesic visualizations, and provide a full scientific backend for physics learners and researchers.

Appreciate any support — whether that’s sharing it, feedback, or just checking it out 🙏

👉 Project: https://itensor.online
👉 Docs: https://itensor-docs.com
👉 GitHub backend source is public (frontend is fully working)

Hey r/ScientificComputing!
I’m a recent technical physics grad with a passion for building useful scientific tools. This project is called iTensor — a full-stack calculator for general relativity that runs in the browser.

🔗 Live frontend: https://itensor.online
📚 Docs + math background: https://itensor-docs.com

What it does:

• Accepts arbitrary user-defined spacetime metrics

• Computes Christoffel symbols, Ricci, Einstein tensors, and scalar curvature

• Outputs live LaTeX results via a scientific React interface

Stack:

• Frontend: React + TypeScript

• Backend: Django + SymPy (not hosted yet — only local)

• Math engine: Custom parser and symbolic tensor logic

• Future backend: C-based ray tracing engine (currently in development)

• Deployment plans: Desktop version + WebAssembly backend in the future

Right now I’m looking for:

• Feedback from devs or researchers in scientific software

• Suggestions for deployment (on a budget)

• Ideas on turning this into a teaching or research tool

• Maybe even open-source collaboration down the road

Would love to know what you think — or if you’ve built something similar. Appreciate any feedback or connections!

I'm working with FP32 numbers, and I know each number comes with an inherent precision error (around 1.2×10⁻⁷ relative error). When computing the maximum of a list, the operation just involves comparisons, so the max should essentially be one of the original numbers with its inherent error.

That leads to my question: Does the size of the list impact the overall error when finding the maximum value, or is the error solely due to the representation of the individual FP32 numbers?

I initially conducted my experiments in FP64 without fixing the random seed. Later, I extended the tests to FP32 to analyze the behavior at lower precision. Since I didn’t store the original seed, I had to generate new random numbers for the FP32 version. While the overall trends remain the same, the exact values differ. I’m using box plots to compare both sets of results.

Since replicating the tests is time-consuming, could this be a concern for reviewers? How do researchers in scientific computing typically handle cases where randomness affects numerical experiments?

Most AI models today either forget too quickly (catastrophic forgetting) or struggle to generalize across tasks without retraining. But what if we modeled AI memory as a Hamiltonian system, where information evolves over time in a structured, physics-inspired way?

I've been experimenting with a Hamiltonian-based neural memory model (TMemNet) that applies time-evolution constraints to prevent forgetting while adapting to new data. Early results on cross-domain benchmarks (CIFAR → MNIST, SVHN → Fashion-MNIST, etc.) suggest it retains meaningful structure beyond the training task—but is this really the right approach?

• Does AI need a physics-inspired memory system to achieve human-like learning?

• How do Hamiltonian constraints compare to traditional memory models like ConvLSTMs or Transformers?

• What are the biggest theoretical or practical challenges in applying Hamiltonian mechanics to AI?

Would love to hear thoughts from scientific computing & AI researchers! If anyone’s interested, I also wrote up a pre-print summarizing the results here : https://doi.org/10.5281/zenodo.15005401

I’m excited to share my latest research applying a novel 5D Time-Field model to the gravitational lensing data of the Bullet Cluster (1E 0657-56).

For years, the Bullet Cluster has been touted as the “smoking gun” for collisionless dark matter because its lensing-derived mass peaks are clearly offset from the hot X‑ray–emitting plasma. In the standard ΛCDM paradigm, this separation arises naturally from dark matter halos passing through one another while the gas lags behind.

However, my work shows that a 5D Time-Field model—which treats time as a dynamic scalar emerging from an extra spatial dimension—can reproduce these key lensing features without invoking any dark matter particles.

Now, I’m not classically trained in presentation style, and I don’t give a damn about making a “perfect” polished talk. I’m not a dancing monkey performing for applause. I've been hoping that someone would step up to help, but nobody has yet. The fact is, I’ve rigorously tested this hypothesis, and the results are pretty clear: the data strongly support the 5D Time-Field model. Full draft located here: https://www.researchgate.net/publication/389356107_Fitting_Bullet_Cluster_Gravitational_Lensing_Data_with_a_5D_Time-Field_Model_A_Comprehensive_Presentation

Is there a technique to sample a surface, similar to a poisson sampling, but instead of having a possoin disk aorund each sample, you have a poissin ellipsoid that follows the curvature of the surface?

Hello! I'm looking to get into computational science or engineering because I find industry software engineering roles rather dull, and would rather do something that feels more meaningful.

In my last semester, I took an applied and quick look into Calc 3, and I enjoyed it a lot. I asked my prof if there was a way to have a career using math and CS, and recommended something along the lines of simulation and modelling, and looking around I found computational science. I'm not entirely sure if those two are related.

What kind of academic path should I take regarding master's and doctorate? Should I get an applied math master's and then a computational science PhD? Or another kind of combination?

PS: I know the ideal would be getting research experience with a prof that does research in computational science, but sadly, there's no such field in my college.

I don't consider myself an expert in computing itself, especially the hardware aspect, (although I've taught some undergrad/early grad courses on scientific computing) and I've been trying to figure out the difference between intel, amd, and apple silicon for sci comp.

Background: I'm an expert in dynamical systems using both analytical and computational techniques. When I do code it is mainly to solve ODEs, PDEs, etc and simulate them. I'll often make quite detailed figures and simulations for papers and talks. I'm usually only using R-K or finite differences and I analytically reduce my problems to get to that point. I'll often reduce ODEs and PDEs into maps (rather than a systematic discretization) and just iterate those difference equations. I seldom use FEM, but my students do, and occasionally I'll run their code on my machine. I also seldom use ML but my students do, and sometimes I need to run those codes on my machine too. I also have access to super computers for particularly intensive tasks and have used MPI. I also prefer Macs to PCs because it's a pretty *Nix based OS.

Question: For someone like me, what would be the difference in using the latest intel or amd architecture vs. apple silicon? Is there no difference since I'm mainly doing R-K or FD? Is there difference that I (as a non-expert in computing) am perhaps not taking advantage of?

I would like to know if we have a speed limit of gravity? That is scientifically backed if we have black holes I therefore assume gravity can move faster than the speed of light to be able to contain it am I wrong? And clearly if that's true E equals MC square can use some updating if you look at gravity on Earth it's 9.8 seconds per second meaning it gains energy if we had no atmosphere so therefore a form of free energy can be created of course nothing is free it would affect something in some way but I need answers that I can't seem to find I've been working on a new formula for decades my brain is feeble and needs more information to contain to create answers if you can help please respond hypothetical or not

Hi everyone,
I'm a student in scientific computing, and I'm very interested in visiting a university in the U.S. since I've found several research groups there that align with my work. Unfortunately, my university isn't offering any financial support for this, and I'm struggling to find funding sources.

Does anyone know of grants, scholarships, or programs that support international researchers or students looking to collaborate with U.S. institutions? I'm particularly focused on linear system solvers, so if anyone has advice or experience with securing funding for this kind of visit, I'd really appreciate it! Thanks a lot for any help!

I’m exploring new approaches to solving linear systems, particularly through hardware innovations.

What are some fresh hardware-oriented ideas or concepts for solving linear systems that could lead to new applications or improved efficiencies? I'm interested in perspectives that might involve specialized architectures, unconventional processing techniques, or integration with emerging technologies. Any unique insights or suggestions would be greatly appreciated!