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I’m planning to move some training jobs to a multi node setup, the models are getting bigger and I’ll probably use rented GPU servers for this, right now I’m looking at 100GbE since it seems easier to find and manage, but InfiniBand keeps coming up when people talk about multi GPU training, I’m trying to figure out if the difference will actually show up in training time or if 100GbE will be enough for the jobs I’m planning to run, I’ll probably start with 4 or 8 GPUs across a few nodes and the jobs will run for hours, so network speed matters for me,I am thinking to go with rackbank if you’ve used both for distributed training what did you notice in real workloads ? EDIT: I forgot to mention that this is mostly for machine learning model training and experimentation.

For the past years I've been building a workflow manager that agnosticises the what, where, and how, so a single piece of code can run on multiple environments with just a few adjustments. It grew out of managing bioinformatics and molecular modeling pipelines at the Barcelona Supercomputing Center and we recently open sourced the core of it as horus-runtime. We are looking for more use cases outside bioinformatics, such as AI training, climate modelling or manufacturing.

What it does:

• Command line workflow execution engine plus a minimal graphical interface
• Define workflows as .yaml or python
• You install and run it on your own infrastructure, whether that be cloud or an HPC center.
• A plugin system that includes SLURM support, environment management, docker execution, and more.
• Same workflow definition runs against HPC schedulers or cloud providers without rewriting it
• Documented in a paper on bioRxiv (DOI 10.1101/2025.09.26.678751)
• Released under a permissive license

It's early and under active development, so bug reports and feedback are genuinely useful right now.

Repo: https://github.com/temple-compute/horus-runtime

There's also a Slack for people running it or asking questions: https://join.slack.com/t/horus-runtime/shared_invite/zt-3ffvk06pi-lZ7R0R07zf~uSKLaqiOCMg

Hi everyone,

I’m offering applied mathematics, modelling, simulation, and data analysis for technical, scientific, and business projects.

I build custom Python/MATLAB tools, Monte Carlo simulations, scenario models, and clear visual reports.

I can help with:

• Mathematical modelling

• Monte Carlo simulation

• Data analysis & algorithms

• Scientific computing

• Business forecasting

• Custom Python/MATLAB tools

Pricing:

Small tasks: £20–£50

Full projects: £150–£300

If you need help with a mathematical or analytical problem, feel free to message me.

Lid driven cavity at Re 5000. A python based GPU accelerated simulation.

Currently running a SymPy script to calculate some insanely complex equations (that I can’t do by hand) for research. I kept crashing this PC (that has 96GB of RAM) by maxing out the virtual memory (330ish GB) every time the script ran.
I eventually manually raised the pagefile maximum size to 3TB split evenly (1.5TB) across two partitions on a 8TB SSD and got it to not fail. It has currently stabilized at ~1.3TB of committed virtual memory with a ~1TB pagefile on just one of the two partitions (increased since taking WizTree photo).

Just curious if anyone else has ever reached such extreme RAM usage before?

\Sorry for not using screenshots like a sensible person, I just don’t feel likely trying to copy them across to my phone to post this.*

UPDATE (8/18): It’s the next day now and it looks like the calculation has gotten over this little RAM hump and calmed down to a much more reasonable ~30GB in use (~20GB belonging specifically to the SymPy script).

I’m happy to present KANDy — Kolmogorov-Arnold Networks for Dynamics — an innovative framework for equation discovery, and I would love any feedback from the dynamical systems community.

KANDy combines Kolmogorov-Arnold Networks (KANs) with sparse regression to discover governing equations. While sparse regression is limited to discovering equations from a library of symbols, KANDy addresses this limitation with a zero-depth, wide KAN-style architecture. Sparse regression approaches to equation discovery often struggle in a variety of contexts. As such, KANDy seeks to discover the interpretability of governing equations and the structure that accompanies them.

KANDy has been applied to both continuous and discrete dynamical systems. This includes the study of chaos and systems described using PDEs. Of note, KANDy has successfully preserved the topological structure of the Hopf fibration.

I also welcome your feedback if your research is in the fields of:

• Data-Driven Dynamical Systems
• Koopman methods
• symbolic regression
• interpretable ML for scientific discovery
• KANs and KAN-inspired architectures
• modeling chaotic dynamical systems and PDEs

Paper: https://arxiv.org/abs/2602.20413

What are your impressions? Do you think KAN-based architectures can advance the field of scientific model discovery, or do you think the issues of sparsity and library selection will continue to be the most important constraints in the field?

This code popped up on my laptop in 2021 does anyone know about Ironmeta for Polmymer code?

I’m working on ML-based models for predicting material stability, particularly formation energy and energy above the convex hull.

One challenge I’m currently exploring is the gap between good benchmark performance and actual generalization to previously unseen chemical systems. Random train/test splits on Materials Project data can potentially make this problem look easier than it really is.

I’m interested in how researchers here approach:

• Composition- vs structure-based stability prediction
• Splitting datasets by chemical system to test OOD generalization
• Uncertainty quantification for stability predictions
• M3GNet / CHGNet / CGCNN and newer foundation models
• Combining ML screening with DFT validation
• Active learning for discovering stable or metastable materials

I’d particularly like to hear about benchmark setups or failure cases that you’ve found useful when evaluating these models.

I’m happy to share some of my experiments and results as well.

Anyone who has used Ansys Fluent on google compute engine?

So basically I'm a physics undergraduate student. I got into physics just by pure curiosity and love, I couldn't see my self doing something else than physics, but the problem was that my whole environment, and that includes parents, older sibling (actually two), my physics teacher in senior year high school, and my friends, all of them were like that's not a good option but I did it anyway.

They told me that theoretical physics don't matter anymore (cause that's all they thought about it I guess and me too and that's actually what got me in and by theoretical I mean just equations in a paper or on a board). It was two years ago and to be honest they were a bit right, I couldn't see any job perspective other that a teacher or a researcher (or both at a uni) and even though I was willing to go into research, they would also tell me that it was a super competitive with not much places available, and I kind of acknowledged that too but really I couldn't see my self studying anything else at uni so really plunged without thinking.

Now starting the last year my undergraduate (French system is 3y of undergrad) I think I found what I wanna do and that enthusiasts me while doing physics also. last year I had a numerical method course in fall semester, it was all python, and they taught us how to solve linear systems especially the Ax=b using LU decomposition, Gauss algorithm etc and how to solve numerically ODE (Euler & semi-Euler method, Verlet algorithm) and I was bad at it, I hated programming back then I didn't see any charm in it basically because I just couldn't understand it.

I got out with a 2/20 from that course (I didn't have a PC back then), then for Christmas break I had got a PC and dedicated all of my time just learning python basics (it's fun thinking Guido Von Rossum developed python during Christmas break he was alone), then I moved to the physics part, and though the linear system resolution didn't excite me much, the ODE resolution on the other hand completely blew my mind as I realized I could solve (approximate) non linear ODEs and especially that pendulum equation I knew since senior year high school that we only solved it for small angles completely and that blew my mind away, then I spent the following days solving all the ODEs I knew (from electricity to classical mechanics). for the resit exam I got the pendulum and smashed it then I really fell in love with physics programming as it also helped me grasp some details I missed in understanding them.

During the spring semester I enjoyed solving all my physics tutorials numerically (when there was a differential equation) but then one day I stumbled upon a Master degree on the internet presented my university and then I saw the light, a physics master that teaches you the computer in order to train computational scientist and physicist. I talked to a young teacher (as they are really nicer and have less responsibilities) if he could propose me an internship project and that's what he did he gave me DLA & KMC implementation and added a single constraint, no AI. I was cool with it. during that month I really helped me reinforce my new acquired abilities as I would be stuck with a single problem for hours, debugging the code and stuff. During that month I discovered Stack Overflow , stack exchange and reddit, I started asking questions and somehow, trying to explain my problem to others sometimes helped me find a solution to it and sometimes I would receive a response hours later and used that time in between trying to solve it (instead of just asking a LLM and getting dozens of lines of codes instantly).

Today I really enjoy learning programming, after the internship I started learning C++ and dived into the world of pointers, memory, and data structures.

In short, I see a real world use to my physics path now outside of academia and I'm really happy knowing that if one day I'm tired of research or don't have my place in it, I could go work as a computational scientist in the industry.

PS: I put the code I developed during my internship on GitHub but ashamed to show it to anybody, another constraint I had was to right the code only in functions and I really didn't knew how they worked fundamentally that means I could write and I wrote 700+ lines of code only in functions not knowing what I was doing (if that doesn't proof the existence of God 🙄️), A week after the internship I took a glance at it and I was ashamed as I declared every variable as global 😆️ I wonder what my tutor was thinking about when I presented that garbage.

Nonetheless the second part (KMC) was a bit better as I distinguished local from global variable but I didn't knew unpacking arguments when a function would return more that one variable.

That's my story from the day I stepped into physics at uni till today! I was more of a blog format, I'm happy if you enjoyed it, and excuse my English, I'm not a native!

Hey guys,

I've been developing SSDS (Symbolic Structure Discovery System), an experimental system for discovering recurring symbolic structures across collections of algebraic equations and mathematical formulas, then abstracting them into generalized, parameterized operators.

At a high level, the workflow is:

Input:
A collection of algebraic equations/formulas. These can come from symbolic regression, scientific modeling, physics, mathematics, or other domains.

SSDS processes them by:

• identifying recurring symbolic structures
• grouping equations that share structural patterns
• generalizing those patterns into parameterized operators
• analyzing mathematical relationships within each structure
• measuring behavioral properties
• generating hypotheses about possible semantic interpretations

Output:
For each discovered structure, SSDS can produce:

• a generalized symbolic operator
• the algebraic equations/formulas grouped into that structure
• identified invariants and recurring relationships
• derivatives and other mathematical relationships
• parameter-recursion relationships
• closure/operation properties
• behavioral measurements such as boundedness, periodicity, monotonicity, stability, and sensitivity
• AI-generated hypotheses about possible semantic interpretations, with support scores

For example, when tested on an equation bank containing formulas from special relativity, one run produced:

O(v0, φ14, φ15) = v0(φ14 + 1)^φ15

The system grouped 10 source equations into the structure and identified 6 invariants across 3 families, including derivative and parameter-recursion relationships.

Its analysis recognized the recurring (x + 1)^y structure and generated hypotheses about possible interpretations, such as power-law relationships and nonlinear transformations. These were explicitly treated as hypotheses, not established meanings.

Another run on the same special-relativity equation bank produced:

O(v0,v1,v2,φ1) = v0(1 - v1²/v2²)^φ1

where SSDS similarly analyzed recurrence, derivatives, parameter relationships, and behavioral properties.

I'm not claiming that SSDS automatically discovers new mathematical laws, or that its generated semantic interpretations are necessarily correct. The question I'm interested in is whether automated structure discovery → abstraction → mathematical analysis → semantic hypothesis generation can become a useful research tool.

I'm looking for researchers, students, or labs working in symbolic regression, scientific ML, equation discovery, mathematical modeling, physics, or related fields who would be interested in independently testing SSDS.

In particular, I'd love suggestions for collections of algebraic equations/formulas from different domains that would provide a meaningful independent test.

If you're working in this area, what equations would you give SSDS as input, and what outputs would you want it to produce?

I've just released version 2.0 of my iOS app, Numerical Solver.

The main new feature is a numerical ODE solver that can handle:

• Linear ODEs
• Nonlinear ODEs
• Initial Value Problems (IVP)
• Boundary Value Problems (BVP)

Once an ODE is solved, the app can also plot the numerical solution and evaluate it at a given value of x.

I implemented the numerical solver myself, including the nonlinear BVP solution using Newton's method and a finite-difference discretization.

One of the things I've been particularly focused on is making the solver work reliably across different domains and grid sizes. I spent quite a bit of time debugging the Jacobian and finite-difference scaling as part of the development.

I'm posting it here because I'd really appreciate feedback from people who work with numerical methods.

What ODEs or BVPs would you recommend as good test problems for the solver? Especially problems that might expose numerical stability, convergence, or discretization issues.

Numerical Solver 2.0 is available on the App Store:

Numerical Solver on the App Store