Yao Lai
@chingyaolai
Assistant Professor @Stanford studying fluids, ML, and the physics of ice & climate change.
Finally published @ScienceMagazine: Can AI help yield new insights from vast amounts of Earth data? We use large-scale data and neural nets to find the constitutive laws of glacial ice, which differ from commonly assumed forms in conventional models. science.org/doi/10.1126/sc…
We're pleased to release DIFFICE-jax v1.0, the foundation of our Science paper and the first #DIFFerentiable #NeuralNetwork solver for #DataAssimilation of ICE shelves written in #JAX: 🔗Docs: diffice-jax.readthedocs.io/en/latest/inde… 📄Peer-reviewed by JOSS #OpenSource: joss.theoj.org/papers/10.2110…
Finally published @ScienceMagazine: Can AI help yield new insights from vast amounts of Earth data? We use large-scale data and neural nets to find the constitutive laws of glacial ice, which differ from commonly assumed forms in conventional models. science.org/doi/10.1126/sc…
From #PhysicsInformedML to #MLInformedPhysics: We're excited about the "knowledge discovery" component of AI utilizing vast amount of data. There is much more out there to be discovered, as Nature's imagination is far greater than that of humans. Don't stop searching.💡…
In Science, researchers report a #physics-informed #DeepLearning model that can predict the deformation behavior of Antarctic ice shelves, revealing complexities of the process that extend beyond the traditional understanding. 📄: scim.ag/3XWfs3W #SciencePerspective:…
In Science, researchers report a #physics-informed #DeepLearning model that can predict the deformation behavior of Antarctic ice shelves, revealing complexities of the process that extend beyond the traditional understanding. 📄: scim.ag/3XWfs3W #SciencePerspective:…
news.stanford.edu/stories/2025/0… NASA Earth data + AI enable us to infer the constitutive models critical for ice dynamics. AI is useful, but no data = no discovery. Below is an example of the training data: a velocity map showing the dynamics of the Antarctic Ice Sheet. Source: #NASA…
Finally published @ScienceMagazine: Can AI help yield new insights from vast amounts of Earth data? We use large-scale data and neural nets to find the constitutive laws of glacial ice, which differ from commonly assumed forms in conventional models. science.org/doi/10.1126/sc…
Looking forward to learning about recent advances in #AI4Climate at the @APSphysics #GlobalPhysicsSummit. Come check out the back-to-back focus sessions, "AI Applications in Weather and Climate I & II," on Tuesday from 9:00 AM to 1:30 PM! summit.aps.org/schedule/?c=ey…
Want to learn about and share the latest progress in #AI4Climate? We invite abstract submissions to our new sessions 26.01.02 and 23.01.10 at the 2025 @APSphysics meeting. Exciting invited talks by Laure Zanna (NYU)@laurezanna, Ashesh Chattopadhyay (UCSC)@ashesh6810, Michael…
Excited to share our new paper in @PNASNews doi.org/10.1073/pnas.2…! Ice cubes often appear cloudy because, as water freezes, air bubbles get trapped and scatter light. But how does freezing rate affect the shape of the bubbles? @UMDResearch @UCSBengineering @PMMH_lab @poftwente
After nearly two weeks of overwhelming uncertainty, today it happened. I was fired from my dream of working at NOAA. I'm so sorry to everyone also affected. My job was to strengthen NOAA's use of machine learning and AI for subseasonal-to-decadal weather and climate prediction
🎉Excited to announce major new breakthroughs in our Aurora foundation model! Our team (@crisbodnar, @ikwess, @a_lucic, @megjanestanley, Anna Vaughan) has achieved unprecedented results across multiple Earth system forecasting tasks. Here's what's new... 🧵
Our new review paper "Machine Learning for Climate Physics and Simulations" with Pedram @turbulentjet, Aditi, Maike, Raffaele & Balaji is online @AnnualReviews 🎉. Beyong accelerating simulations, can ML help us understand climate physics? We highlight the recent progresses &…
