📢 Calling for (4-page) workshop papers on hashtag #differentiable programming and hashtag #SciML @euripsconf.bsky.social.

It may only be a band-aid, but we have just announced our new "Salon des Refusés" sessions for papers rejected due to space constraints: bsky.app/profile/euri...

Check Out my latest video on implementing an attention-based neural operator/emulator (i.e. a Transformer) in JAX: youtu.be/GVVWpyvXq_s

Travelling to Singapore next week for #ICLR2025 presenting this paper (Sat 3 pm nr. 538): arxiv.org/abs/2502.19611 DM me (Whova, Email or bsky) if you want to chat about (autoregressive) neural emulators/operators for PDE, autodiff, differentiable physics, numerical solvers etc. 😊

Check out my latest video on approximating the full Lyapunov spectrum for the Lorenz system: youtu.be/Enves8MDwms Nice showcase of #JAX's features: - `jax.lax.scan` for autoregressive rollout - `jax.linearize` repeated jvp - `jax.vmap`: automatic vectorization

Art.

Today, I had the chance to present my #NeurIPS paper "APEBench" @SimAI4Science . You can find the recording on YouTube: youtu.be/wie-SzD6AJE

Thanks @munichcenterml.bsky.social for highlighting my recent #NeurIPS paper: APEBench, a new benchmark suite for autoregressive emulators of PDEs to understand how we might solve the models of nature more efficiently. More details 🧵 Visual summary on project page: tum-pbs.github.io/apebench-pap...

Our online book on systems principles of LLM scaling is live at jax-ml.github.io/scaling-book/ We hope that it helps you make the most of your computing resources. Enjoy!

I’d like to thank everyone contributing to our five accepted ICLR papers for the hard work! Great job everyone 👍 Here’s a quick list, stay tuned for details & code in the upcoming weeks…

I created a video to help you get started using the APEBench suite (my recent #neurips paper) to benchmark autoregressive neural emulators for PDEs with a simple ConvNet emulation of 1D advection: youtu.be/q8fjQ4ZFynw

Happy new year! 🎉 Two days ago we entered 2025 and just in time the channel surpassed 25k subscribers. Wow! Thanks to everyone for their kind words and support along the way: www.youtube.com/channel/UCh0...

Check out my latest video on approximating the largest Lyapunov exponent of a dynamical system by integrating a tangent linear perturbation dynamic via autodiff in JAX: youtu.be/zRMBIkpcuu0 Very neat use-case of forward-mode AD for efficient Lyap approximation.

Automatic differentiation in forward mode computes derivatives by breaking down functions into elem operations and propagating derivatives alongside values. It’s efficient for functions with fewer inputs than outputs and for Jacobian-vect prod, using for instance dual numbers.

Now presenting APEBench at #NeurIPS in West #5407.

Excited to be at NeurIPS this week! 🎉 I'm part of four exciting projects being presented: - The Well & Multimodal Universe: massive, curated scientific datasets - LaSR: LLM concept evolution for symbolic regression - MPP: 0th gen @polymathicai.bsky.social All posters Wed/Thu - stop by! 👋

I am at #NEURIPS and will present it today Wed 11 at 11:00am, West Ballroom A-D #6001

I will be presenting my poster on APEBench on Thursday from 11:00 to 14:00 PST at West Ballroom A-D #5407. This was done as part of my PhD with @thuereygroup.bsky.social in collaboration with my talented co-author, Simon Niedermayr, who is supervised by Rüdiger Westermann.