Philip Mocz

Interested in scientific computing and astro/physics simulations? Follow me on Medium to explore college intro-level #Python tutorials @philip-mocz" target="_blank" rel="nofollow noopener">medium.com/@philip-mocz

Try it in your browser: pmocz.github.io/two_button_cal…

Try out the new Two-Button Calculator proposed in Odrzywołek (2026), interactive demo here: 👉🔴🟡github.com/pmocz/two_butt…

@astro_magnetism @the_IAS Congrats, James! Excited to see what's next for you!

This was the news! I’ll be taking the Hubble to the Institute of Advanced Study (@the_IAS) to continue my work on understanding magnetic fields and turbulence across a broad range of contexts!

Solving block tridiagonal matrices is a common bottleneck for physics solvers. Started prototyping a way to do this fast in #JAX w/ a parallel GPU/batched-LU algorithm: github.com/pmocz/blocktrix Already seeing huge gains over block Thomas (gpu) algo. @claudeai code is helping out

LLMs can be an "Expert-In-The-Loop" for physics problems that require simulation. Here's a minimal example using #TextGrad (Automatic “Differentiation” via Text) solving a simple physics problem iteratively: github.com/pmocz/tesserac… @SimAI4Science #TesseractHackathon

Here's a minimal example of how differentiable solvers can find blow-up solutions: github.com/pmocz/blowup #JAX #OptimizationOfDIscreteLoss

We are off to a great start with our first set of 9 #EurIPS workshops! the lecture rooms are buzzing and conversations are flowing at our three different workshop locations!

Sharing a small codlet for how to create an end-to-end differentiable finite volume ALE fluid solver in #JAX : github.com/pmocz/euler-ale

@cosmo_shirley @mikemccabe210 @PayelMukhopadh3 @__tm__157 @BrunoRegaldo @FrancoisRozet @PolymathicAI This looks incredible~!

So proud to see the culmination of effort led by @mikemccabe210 @PayelMukhopadh3 @__tm__157 @BrunoRegaldo @FrancoisRozet along with the amazing team at @PolymathicAI to produce The first "Polymathic"/cross-disciplinary AI model for fluid dynamics! We started with creating The Well, the first internet scale of fluids dataset co-led by @mikemccabe210 and @oharub with the goal of creating the first cross-disciplinary fluid model! Now it is here, and we are lovingly naming it the Polymathic Walrus 😁!

Having a lot of fun scaling up massive #Python / #JAX physics simulations on @ORNL's Frontier supercomputer with @AMD MI250X GPUs -- currently hitting 3 billion cell-updates/sec on a 2048³ run. 🚀 #HPC #Supercomputing #JAX

I'm sharing a blog post going along with the code: philip-mocz.medium.com/jaxion-differe…

I'm excited to share jaxion, a differentiable Python/JAX library for fuzzy dark matter (axions) + gas + stars, scalable on multiple GPUs ⭐️repo: github.com/JaxionProject/… 📚docs: jaxion.readthedocs.io Feedback + collaborations welcome!

@AlbertEinstein Might have something to do with this ... (ref: github.com/JaxionProject/…)

This is one of the most famous equations in physics. Do you know what it is? Drop your answer in the comments.

We're hiring Flatiron Research Fellows & Software Fellows at the @FlatironInst CCA. These independent postdoc positions offer outstanding opportunities for collaboration, computation, and scientific growth in NYC: apply.interfolio.com/173402 apply.interfolio.com/173403

I recently ran into errors where the @Google search bar gives me numerically incorrect answers for unit conversions or useless AI results. No idea what is causing the first issue. It used to be rock solid. With #vibecoding I could quickly create a new solution.

I noticed Google search is no longer reliable for quick unit conversions & calculations. So I built a simple online calculator Give it a spin: pmocz.github.io/online-unit-co…

@astro_magnetism Really nice work, James!

🤩🤩🤩

@REasther Fair, it's mainly backprop/reverse-mode autodiff here. A cool related topic, though, is adding neural nets to differentiable simulations as trainable subgrid models.

@PMocz is it really "AI" in this context, tho?

Several groups are exploring this idea: e.g. arxiv.org/abs/2506.19243, arxiv.org/abs/2201.06780

AI-powered differentiable physics is unlocking new ways to explore PDEs -- and may even one day solve a huge unsolved problem in mathematics.

I'm sharing a quick toy code in #JAX illustrating the concept of differentiable sims: github.com/pmocz/navier-s…