New 20hr bootcamp on Probability & Statistics!!! Videos released weekly but full playlist already posted: https://t.co/3LnVCaeYGv Probability & Statistics are cornerstones of data science and machine learning. This course rapidly covers the basics and gets into advanced topics.

1/ Today with my colleagues @PolymathicAI, I'm excited to release our latest project, Walrus, a cross-domain foundation model for physical dynamics, into the world. https://t.co/ihv1MZGQM3 Paper: https://t.co/d6ah9LO4ud Git: https://t.co/s3p8qGhZQR HF: https://t.co/RufaBD9eJk

NEAR 40% UP - HAS STIL ONVER 250% ROOM TO GO ! $NXXT $NVDA $TSLA $AMC $GME

So proud to see the culmination of effort led by @mikemccabe210 @PayelMukhopadh3 @__tm__157 @BrunoRegaldo @FrancoisRozet along with the amazing team at @PolymathicAI to produce The first "Polymathic"/cross-disciplinary AI model for fluid dynamics! We started with creating

I’m excited to announce that @PolymathicAI new astrophysics model, AION-1, has been accepted to #neurips2025 ! 🎉 Come see our poster on Dec 5 (Friday) at 1-4pm PT, poster session 5 OR hear our talk from @liamhparker and Francois Lanusse at #AI4Science workshop It’s the

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. 👉

Create your own prediction markets! Any market, any topic, live in <15 seconds.

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?

I now have a full @YouTube playlist of video lectures with coding demonstrations that accompanies every chapter of my book! Check it out here:

New Video Series: Statistics & Data Analysis! https://t.co/YfJTxDeiHn 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?

Give kids a break from iPhones and tablets this Christmas with the LED Note Board — a glowing, hands-on way to draw, write, and create without screens.

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 🔗 https://t.co/EuxThqq8zf

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:

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 https://t.co/28BG4C6Bdx #FlightTech #Turbulence #ClimateAction #AviationInnovation

Bitcoin: I’m keeping it practical. Still a bear flag. Daily chart hasn’t changed + Larsson Line is still blue. Then we can argue which scenario it will go 👇

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 https://t.co/fBA3vhEBKc #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:

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) https://t.co/AuWFmVal8o

🌀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 🔗 https://t.co/MPckvSdsEs #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. 🥳 https://t.co/ngiB8zcnv2