Steven Brunton

New 20hr bootcamp on Probability & Statistics!!! Videos released weekly but full playlist already posted: youtube.com/watch?v=sQqnia… Probability & Statistics are cornerstones of data science and machine learning. This course rapidly covers the basics and gets into advanced topics.

We present Panda: a foundation model for nonlinear dynamics pretrained on 20,000 chaotic ODE discovered via evolutionary search. Panda zero-shot forecasts unseen ODE best-in-class, and can forecast PDE despite having never seen them during training (1/8) arxiv.org/abs/2505.13755

🌀Just out in @NatMachIntell: Can generative AI enhance fluid simulations? We explore how diffusion models enable fast, interpretable, stochastic flow modelling, reshaping the simulation pipeline. 📝 With Luca Guastoni 🔗 nature.com/articles/s4225… #FluidMech #AI #DiffusionModels

General relativity 🤝 neural fields This simulation of a black hole is coming from our neural networks 🚀 We introduce Einstein Fields, a compact NN representation for 4D numerical relativity. EinFields are designed to handle the tensorial properties of GR and its derivatives.

New Book & Video Series!!! (late 2025) Optimization Bootcamp: Applications in Machine Learning, Control, and Inverse Problems Comment for a sneak peak to help proofread and I'll DM (proof reading, typos, HW problems, all get acknowledgment in book!)

🎉 Great news: Our Machine Learning and Physical Sciences workshop at @NeurIPSConf will be back again this year! 🎉 Keep an eye out for updates on deadlines etc, we will be updating the website soon ml4physicalsciences.github.io #ML4PS2025 @ML4PhyS

Honored to receive the 2025 Harleman Lecture Award at the #IAHR2025 World Congress in Singapore 🇸🇬 Grateful to the IAHR community and proud to contribute to the future of AI for fluid mechanics! #AI #ML #FluidMechanics #Sustainability #Singapore

The final chapter of my book is on autoencoder neural networks. This lecture starts with the theory: youtube.com/watch?v=tn61dh…

Releasing the Energy-Book 🔋 from its first appendix's chapter, where I explain how I create my figures. 🎨 Feel free to report errors via the issues' tracker, contribute to the exercises, and show me what you can draw, via the discussion section. 🥳 github.com/Atcold/Energy-…

Plankton of symmetric attractors Made with #sympy #numpy #matplotlib

Today, NSF announced an add’l 500 NSF Graduate Research Fellowship Program awardees for the 2025-2026 cohort, bringing the total to approx 1,500. #NSFGRFP supports grad students as they pursue their dreams, build STEM skills, & become the next generation of innovators & leaders.

We now have another open Ph.D. position at ETH Zurich in data-driven nonlinear reduced-order modelling, with applications in system ID and control. Interested candidates may apply here: jobs.ethz.ch/job/view/JOPG_…

Apply to Our New Online Graduate Certificate: Data-Driven Dynamical Systems & Controls! 4 Brand New Courses: Foundations of Data-Driven Dynamics & Controls Data-Driven Dynamic Systems Data-Driven Controls Data-Driven Sensing me.washington.edu/future-student… App deadline: July 1, 2025

🎉 Honored to be selected as the Donald R.F. Harleman Keynote Speaker at the #IAHRWorldCongress in Singapore! I'll present our work on turbulence control with explainable deep learning, where #AI meets #WaterScience & #Ecohydraulics. Looking forward! 🌊🤖 #XAI #FluidMechanics

New in @JFluidMech! 🛩️ We study opposition control (OC) for turbulent boundary layers over wings with adverse pressure gradients: OC achieves up to 40% drag reduction! ✈️ 📖 doi.org/10.1017/jfm.20… 👨‍💻 With Y. Wang and M. Atzori !! #CFD #Turbulence #FlowControl #DragReduction

It’s a wrap! It was an honor delivering the closing keynote at the ECCOMAS Math 2 Product conference celebrated at my Alma mater, @UPV!!

New Graduate Certificate in Data-Driven Dynamical Systems & Controls! Come join us, Fall 2025! Topics include: Data-driven dynamics, control, & sensing with modern machine learning Stacks towards Masters degree at UW me.washington.edu/future-student… Application deadline: July 1, 2025

Finally, a Pope who can understand how sinful is to treat differentials as fractions. I’ll be waiting for the papal bull on linear operators.

BREAKING: the new pope is a statistics nerd 😭

Physics Informed Machine Learning with applications in Materials, by @UW expert, Prof. Navid Zobeiry Coolest new series on Youtube! Thanks Navid for sharing your deep knowledge with us! 🙏🧠🤯 youtube.com/watch?v=ak2oTk… All code/slides: composites.uw.edu/AI/

🎥 New video out! Spatio-temporal decompositions for ROMs! See how algebra + ML help build powerful reduced-order models. 🔗 youtu.be/_JkaX5pB1ZQ #ROM #MachineLearning #ModelReduction #DataDriven #YouTubeScience