(1) Today we're releasing Muse Spark 1.1 -- a strong agentic and coding model at a very low price. It's available through our new Meta Model API and in Meta AI.
born to say: damn I got my phd finally😭
forced to say: i’m thrilled to announce that i successfully defended my phd thesis from @MIT_CSAIL and joined @OpenAI as a research scientist a while ago 🥳 stay tuned
It's the era of AI4Sci now where large computational models are revolutionizing the paradigm of scientific discovery! It reminds me how amazed I was when Scientific Generative Agent (our ICML paper) optimized the first batch of physical laws - the future is here:
BREAKING NEWS
The Royal Swedish Academy of Sciences has decided to award the 2024 #NobelPrize in Chemistry with one half to David Baker “for computational protein design” and the other half jointly to Demis Hassabis and John M. Jumper “for protein structure prediction.”
MIT researchers combine classical numerical simulation with neural networks, generalizing large-scale multiphysics scenes and potentially closing sim-to-real gaps in robot manipulation & general control in complex physics: bit.ly/3O6HfdB
Code: github.com/PingchuanMa/NC…
Happy to introduce KAN 2.0: tailor your KAN for scientific discovery. By baking physical priors into your model, KAN evolved to be more powerful in science than ever.
Excited to share our new paper KAN 2.0: Kolmogorov-Arnold Networks meet Science 🚀
The problem with AI + Science is that these two disciplines use different "languages" (connectionism vs symbolism). KAN 2.0 attempts to unify them: smooth transitions from science to KAN and back.
#ICLR22 Is it possible to create digital twins by just watching videos alone? Please check out our RISP framework that allows solving this challenging inverse problem by integrating deep representation learning with differentiable simulation and rendering!
It’s a great pleasure to work with Minghao. Please stop by our NeurIPS Spotlight at East Exhibit Hall A-C #2409 on Thursday 11AM PT. gmh14.github.io/phys-comp/
Excited to share PhysComp (accepted by NeurIPS 2024 as spotlight) that turns single images into 3D objects designed to survive real-world forces! Reconstructing 3D shapes from an image often aims to be beyond visualization—they’re used in gaming, design, and engineering. Yet,
#ICML Please check out "Neural Consititutive Laws." Our solution combines neural network and traditional PDE approach for learning physical dynamics that can generalize to large-scale multiphysics scenes!
Code: github.com/PingchuanMa/NC…
The United States will be powerfully supporting those industries, like Airlines and others, that are particularly affected by the Chinese Virus. We will be stronger than ever before!
Everything you love about generative models — now powered by real physics!
Announcing the Genesis project — after a 24-month large-scale research collaboration involving over 20 research labs — a generative physics engine able to generate 4D dynamical worlds powered by a physics
Fluid is highly dynamical. Have you wondered if they can differentiate and support optimization at scale? Yifei will present NeuralFluid at NeurIPS: tinyurl.com/NeuralFluid
Greetings from 🍁Vancouver at #NeurIPS2024! We’re thrilled to present NeuralFluid: Neural Fluidic System Design and Control with Differentiable Simulation at our poster session. 💧⚙️
Catch me on Thu 12 Dec, 11a-2p East Hall #1511📌
Project🔗: tinyurl.com/NeuralFluid
