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Steven Brunton
@eigensteve
Teaches math to engineers: youtube.com/c/eigensteve Professor @UW researching #MachineLearning for #Dynamics and #Control, especially for #FluidDynamics.
Joined April 2016
Posts
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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.
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    Just finished filming an entire 8 week series on Differential Equations and Dynamical Systems!! This is one of my favorite topics in all of math. And this finishes up all the videos for a two-quarter, 60 hour set of lectures on Engineering Mathematics! youtube.com/watch?v=9fQkLQ…
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    Excited to drop a new 2-week video series: Crash Course in Complex Analysis!!! This is a super useful topic that comes up everywhere in mathematical physics, differential equations, and modern scientific computing. Check it out! youtube.com/watch?v=_mv0q7…
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    My favorite topic in dynamical systems: Chaos!!! This is what got me interested in applied math as an undergrad. The three body problem in planetary dynamics, the double pendulum, turbulence, and more! youtube.com/watch?v=PDeN3i…
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    New video! Partial Differential Equation (PDE) Overview youtube.com/watch?v=pvrIag… Super excited to be deriving and solving PDEs in this series!!! Next up: heat equation in 1D, 2D, ND; separation of variables; wave equation; Navier-Stokes equations, and more!!!
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    Vector calculus & PDEs! youtu.be/Jt5R-Tm8cV8 Powerful & beautiful intersection of math and physics. How to encode physical conservation laws as partial differential equations. And still relevant for modern machine learning! (~20 vid series... how I wish I'd learned it)
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    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!
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    Big news today: Nathan Kutz and I have finished all of the video lectures for our book "Data Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control"! All videos for every section are free on youtube and at our book website databookuw.com
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    FREE PDF for 2ND EDITION OF OUR BOOK!!! databookuw.com/databookV2.pdf We are committed to open science and open education: Free book: databookuw.com/databookV2.pdf Free code (github.com/dynamicslab/) Free lectures (youtube.com/c/eigensteve/) x.com/eigensteve/sta…
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    2nd ed of "Data Driven Science and Engineering" out this summer!!!! New to 2nd ed: * Python+Matlab Code * New Chapters on Reinforcement Learning & Physics Informed Machine Learning * New Sections Throughout * Extensive Homework cambridge.org/brunton-kutz x.com/eigensteve/sta…
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    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!)
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    Its nice to see the recent excitement around Q-learning! Here's a clip from a video all about Deep Reinforcement Learning, including Deep Q Learning youtube.com/watch?v=wDVtea… #machinelearning #ai
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    First new video after being back from Sabbatical!! PDE 101: Separation of Variables... or how I learned to stop worrying and solve Laplace's equation One of the most important concepts in all of partial differential equations youtube.com/watch?v=VjWtMl…
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    Numerical Integration to Simulate Nonlinear Dynamics! The final phase of our series on Differential Equations youtube.com/watch?v=QBeNXH… Topics #Chaos Forward/backward Euler Stability & accuracy Runge-Kutta Symplectic & variational integrators Uncertainty propagation Code examples
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    New on arXiv!!! 🚨🚨🔥🔥🤯🤯 A Unified Framework to Enforce, Discover, and Promote Symmetry in Machine Learning by Sam Otto, with N. Zolman, N. Kutz, & myself A tour de force in machine learning & differential geometry arxiv.org/abs/2311.00212 (w/ beautiful drawings by Sam)