Steven Brunton (@eigensteve) header
Steven Brunton (@eigensteve) profile picture

Teaches math to engineers: https://nitter.cf/t.co/TJ5i3Pg678 Professor @UW researching #MachineLearning for #Dynamics and #Control, especially for #FluidDynamics.

Joined April 2016
Photo by Steven Brunton (@eigensteve)Photo by Steven Brunton (@eigensteve)Photo by Steven Brunton (@eigensteve)Photo by Steven Brunton (@eigensteve)Photo by Steven Brunton (@eigensteve)Photo by Steven Brunton (@eigensteve)Photo by Steven Brunton (@eigensteve)Photo by Steven Brunton (@eigensteve)Photo by Steven Brunton (@eigensteve)Photo by Steven Brunton (@eigensteve)
HydroGym: A Reinforcement Learning Platform for Fluid Dynamics Now published in Nature!! nature.com/articles/s41586-0… GitHub: github.com/dynamicslab/hydro… Amazing collaboration with Christian Lagemann, S Mokbel, M Gondrum, M Rüttgers, Y Wang, P Suárez, L Paehler, D A Bezgin, A B Buhendwa, J L Callaham, S Ahnert, N Zolman, X Shao, J-Ch Loiseau, N A. Adams, M Meinke, W Schröder, K Lagemann, E Lagemann, R Vinuesa & S L Brunton
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Steven Brunton retweeted
A bestiary of chaotic attractors, each carrying its own dihedral Dₙ symmetry. Every disk holds a different attractor of the generalized Field–Golubitsky map: z ↦ {a₀ + a₁|z|² + a₂ Re(zⁿ) + a₃|z| Re[(z/|z|)ⁿᵖ]} z + a₄ z̄ⁿ⁻¹ #builtWithMarimo
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I really enjoyed taking part in this discussion, especially at this moment in time. Thanks Lex, Alex, and Ankur for the great conversation!
This is an incredible moment in this history of mathematics, computer science, physics & science with some of the hardest problems in human history being solved by AI over a period of weeks. Depending on your perspective, it's either a crisis, a triumph, or both. I don't think anyone knows for sure. We're all trying to make sense of it. I oscillate between fear/dread and optimism/excitement on a daily basis. The rate of progress is insane, including today's announcement from OpenAI of more problems solved. To help make sense of all of it, here's my conversation with three mathematicians about this moment. Each of them is an expert in the field of each of the biggest problems solved: Navier-Stokes, quasi-Riemann hypothesis, the unique games conjecture and complexity theory more generally. Ankur Moitra is a mathematician and theoretical computer scientist at MIT, visiting the Institute for Advanced Study in Princeton. Steven Brunton (@eigensteve) is a mathematician and fluid dynamics expert at UW. Alex Kontorovich (@AlexKontorovich) is a mathematician at Rutgers specializing in number theory.
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Steven Brunton retweeted
This is an incredible moment in this history of mathematics, computer science, physics & science with some of the hardest problems in human history being solved by AI over a period of weeks. Depending on your perspective, it's either a crisis, a triumph, or both. I don't think anyone knows for sure. We're all trying to make sense of it. I oscillate between fear/dread and optimism/excitement on a daily basis. The rate of progress is insane, including today's announcement from OpenAI of more problems solved. To help make sense of all of it, here's my conversation with three mathematicians about this moment. Each of them is an expert in the field of each of the biggest problems solved: Navier-Stokes, quasi-Riemann hypothesis, the unique games conjecture and complexity theory more generally. Ankur Moitra is a mathematician and theoretical computer scientist at MIT, visiting the Institute for Advanced Study in Princeton. Steven Brunton (@eigensteve) is a mathematician and fluid dynamics expert at UW. Alex Kontorovich (@AlexKontorovich) is a mathematician at Rutgers specializing in number theory.
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Steven Brunton retweeted
This is a fantastic video explaining orbital dynamics and the resulting economics, and security considerations of cislunar space. Well worth the 8.5 minutes to watch. youtu.be/kdpOdSMzXTA?is=By6z…
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Steven Brunton retweeted
Nice to see the Earth-Moon cycler orbits our lab recently discovered featured in @GarretsonPeter's overview of the cislunar space economy. Lots of interesting possibilities for transportation, surveillance, and infrastructure.
This is a fantastic video explaining orbital dynamics and the resulting economics, and security considerations of cislunar space. Well worth the 8.5 minutes to watch. youtu.be/kdpOdSMzXTA?is=By6z…
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Steven Brunton retweeted
Launched our new AI Institute in Dynamic Systems, funded with $20M by @NSF! We will push generative AI methods to dramatically reduce the aerodynamic design cycle, together with @Boeing! aero.engin.umich.edu/2026/10… #AI4Science #GoBlue! @eigensteve 🔵 🟡
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Steven Brunton retweeted
U-M Prof. @ricardovinuesa is one of the leaders of a new $20M NSF grant to support the AI Institute in Dynamic Systems. Led by the University of Washington, the institute brings together academic and industry partners to develop new ways to model and simulate complex systems.
The @NSF has awarded $20 million to the AI Institute in Dynamic Systems to advance fundamental research on complex robotic manufacturing systems, with an emphasis on aerospace applications. bit.ly/4zrjuSd
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Steven Brunton retweeted
The @NSF has awarded $20 million to the AI Institute in Dynamic Systems to advance fundamental research on complex robotic manufacturing systems, with an emphasis on aerospace applications. bit.ly/4zrjuSd
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The passing of a legend. Less well known is that she coded and simulated the ODEs that Lorenz ended up using for the “Lorenz” system (with Linda Fetter).
We’ve lost an icon. Margaret Hamilton, a computer science pioneer best known for her work at MIT leading development of the flight software for the Apollo missions to the moon, has died at 90. Read about her legacy via MIT News: news.mit.edu/2026/margaret-h…
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Steven Brunton retweeted
Very proud of my PhD students Yuning Wang and Pol Suárez Morales. Our recent work on #DRL for turbulence control, HydroGym, has made it to the cover of @Nature!! Paper: nature.com/articles/s41586-0… #AI4SCience
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Steven Brunton retweeted
It's Fortran Thursday! 🔥 Why does my OpenMP Jacobi stop scaling past 2 threads on a 2048² grid? A roofline model built from 3 likwid numbers predicts it: 279 sweeps/s vs 272 measured. Memory-bound, not a bug. loiseaujc.github.io/posts/bl… #Fortran #HPC
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Awesome work, and really interesting collaboration!!!
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Steven Brunton retweeted
Rivers, pendulums and the thalamus: how the brain balances stable and flexible dynamics, and what changes when consciousness fades. Our latest journey into how the thalamus is shaping cortical dynamics in @ScienceAdvances 🧵1/10 science.org/doi/10.1126/scia…
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Steven Brunton retweeted
The Navier-Stokes equations are among the most important — and most challenging — equations in physics. So, what makes them so difficult? And how could a new OpenAI proof finally resolve the problem? Watch our new video: youtube.com/watch?v=PsWR1rQE…
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Steven Brunton retweeted
Very nice video by @MichiganAero highlighting the research in the VinuesaLab! Thanks to everyone in my group as well for your excellent work! More on understanding and control turbulent flows and on generative AI for aerodynamic design and optimization!! youtube.com/watch?v=cbamiovW…
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Very cool benchmark environment for robot learning!! The NSF AI Institute in Dynamic Systems is optimistically developing benchmark environments for learning across the engineering and natural sciences -- try this one out!!
GPT-6 Astra is impressive, but agentic robotics ≠ just control. Physical agents should control robots, learn new skills, and design their own hardware. So we introduce RLE-Bench: 48 everyday robotics engineering tasks spanning closed-loop control, policy learning, perception, and mechanical design.
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Steven Brunton retweeted
GPT-6 Astra is impressive, but agentic robotics ≠ just control. Physical agents should control robots, learn new skills, and design their own hardware. So we introduce RLE-Bench: 48 everyday robotics engineering tasks spanning closed-loop control, policy learning, perception, and mechanical design.
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Steven Brunton retweeted
New post: quantifying Gauss-Seidel's loop-carried dependency with OSACA, and a loop-unrolling fix that cuts it from 12 to 2 cycles/element. Three compiler experiments included to isolate the effect from vectorization. 👉 tinyurl.com/3h79t8fn
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Steven Brunton retweeted
With all the Navier–Stokes excitement, I almost forgot to say that our tiny billiard ball has made it to @PNASNews — and yes, it can run a universal computer! 🎱💻 Undecidability already lives in dimension two. Today with @Isaacramr__ at PNAS. pnas.org/doi/10.1073/pnas.26…
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Steven Brunton retweeted
This shall be an inflection point for us. Night helped me clarify my thoughts regarding the Navier-Stokes: 1. Martínez-Zoroa and Córdoba deserve the Fields in addition to other prizes. We should be honoring their work. 2. They Clay could (is it possible?): (i) be shared between M-Z and Córdoba (80%) + Buckmaster and Alpöge (20% -even when, personally, it feels bittersweet) -inclusive- or (ii) restated: no external-force. 3. Open AI shall face severe consequences if accusations are true. 4. We, the individuals of a small community -just us: math/math phys. students, professors, researchers-, independently from institutions, governments and companies, may hold closed-private but informal meetings to decide how we are willing to work from now on. I know some of us will choose to pursue a modest path: free from the publish-or-perish practice, AI proofs, stupid competition for recognition and money, etc. From that, we can build something real. 5. We, as a whole society, must be aware of the plausible future scenarios whether the statu quo remains unchanged: who has the majority of agency (power) regarding public affairs including knowledge, education, technology, violence control, justice management, etc.? Is it optimal or even safe? Meanwhile, let’s honor the work of those who deserve it: arxiv.org/pdf/2309.08495 arxiv.org/pdf/2308.12197 arxiv.org/pdf/2207.14385 arxiv.org/pdf/2410.22920
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