Introducing Langevin Flow Maps TLDR: Neural Network Potentials that learn their own stochastic pathwise solution for faster simulation. - Transferable across molecular systems. - Transition dynamics are preserved. - No trajectory data are required. Work together with @sgmccallum, supervised by @AlexanderTong7, and @JamesFosterBath. Arxiv: arxiv.org/abs/2610.05998 Data: huggingface.co/datasets/nikl… Code: github.com/sammccallum/lfm

Strong Stochastic Flow Maps has been accepted to #NeurIPS2026 as an Oral! 🎉

Introducing Strong Stochastic Flow Maps TLDR: Stochastic Flow Maps where we learn the stochastic solution path. Work led by Sam McCallum, @zwblasingame, with Timothy Herschelll, @AlexanderTong7, and @JamesFosterBath Arxiv: arxiv.org/pdf/2606.01086 Code: github.com/sammccallum/ssfm

Great news! James Foster has been awarded the Leslie Fox Prize for Numerical Analysis — a globally recognized honor for early-career researchers in the field — for his work on the analysis of splitting methods for stochastic differential equations (SDEs). Congratulations!

✨We have a new release of Diffrax! GitHub: github.com/patrick-kidger/di… 🎲 Stochastic Runge--Kutta methods. 📊 Progress bars. 📝 New paper on arXiv (!!!) We did some cool new numerical Brownian stuff to make higher-order adaptive SDE solvers work! ⚡️

(Numerical SDE geeks, we have a write-up of these shiny new SRK methods in our devdocs here: docs.kidger.site/diffrax/dev… )