Steven Brunton
@eigensteve
Teaches math to engineers: http://youtube.com/c/eigensteve Professor @UW researching #MachineLearning for #Dynamics and #Control, especially for #FluidDynamics.
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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.
Had an interesting discussion with a colleague about the merits of #Fortran and #Python for an undergrad class (ME not CS) on numerical linear algebra. Having just migrated my personal webpage, I figured I'll start a technical blog and write about it. 👉 tinyurl.com/3a4dfcnu
Extremely proud of our recent "Lost in Latent Space" paper by our amazing @PolymathicAI intern @FrancoisRozet ! How far do you think we can/should compress ? What is needed in the latent space to keep rolling out the physics right across so many scales?
Does a smaller latent space lead to worse generation in latent diffusion models? Not necessarily! We show that LDMs are extremely robust to a wide range of compression rates (10-1000x) in the context of physics emulation. We got lost in latent space. Join us 👇
I now have a full @YouTube playlist of video lectures with coding demonstrations that accompanies every chapter of my book! Check it out here: youtube.com/watch?v=A1AvIj…
youtube.com
YouTube
BOOK ANNOUNCEMENT: Data-Driven Methods for Dynamic Systems
New Video Series: Statistics & Data Analysis! youtube.com/watch?v=QIXUTs… 35 videos, 10 hours: Random sampling, Central limit theorem, Distribution estimation, Method of moments, Maximum likelihood estimation, Hypothesis testing, Monte Carlo sampling, Bayesian statistics, and more!
100k on my mathematics channel !! 🥹 what video should i do to celebrate?
What if a spacecraft could cycle between Earth and Moon orbits, performing multiple circuits of each, naturally and indefinitely, with zero propulsion? We’ve discovered a new class of stable, prograde, low-energy cycler orbits that do just that. Why these orbits matter:…
🧠💨 New from #VinuesaLab in @CommsEng (@NaturePortfolio): We use multi-agent #DRL to control 3D turbulent flows, achieving up to 21% drag reduction on cylinders! First-ever MARL training on 3D cylinders 🚥 🌀 Stabilized wakes 💡 Efficient control 🔗 nature.com/articles/s4417…
I've done it! I've finished the complete series of video lectures to complement my book. Here's the last one on model identification with autoencoders: youtube.com/watch?v=cFpyjE…
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YouTube
Model Discovery with Autoencoders - Data-Driven Dynamics | Lecture 27
Honored to be featured in @BBCFuture’s article: “How aircraft designers are beating rising turbulence” ! ✈️ Proud to play a part in pushing aviation safety forward with XAI and DRL!! @BBC bbc.com/future/article… #FlightTech #Turbulence #ClimateAction #AviationInnovation
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…
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YouTube
Topological Conjugacies - Data-Driven Dynamics | Lecture 22
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
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-…
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_…
United States Tendances
- 1. Jets 79.5K posts
- 2. Jets 79.5K posts
- 3. Justin Fields 8,278 posts
- 4. Aaron Glenn 4,338 posts
- 5. #HardRockBet 3,331 posts
- 6. Sean Payton 2,027 posts
- 7. London 202K posts
- 8. Garrett Wilson 3,243 posts
- 9. Bo Nix 3,301 posts
- 10. HAPPY BIRTHDAY JIMIN 147K posts
- 11. Tyrod 1,536 posts
- 12. #OurMuseJimin 192K posts
- 13. #DENvsNYJ 2,079 posts
- 14. #JetUp 1,916 posts
- 15. Peart 1,897 posts
- 16. #30YearsofLove 168K posts
- 17. Bam Knight N/A
- 18. Kurt Warner N/A
- 19. Rich Eisen N/A
- 20. Anthony Richardson N/A
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