Quick reminder that the deadline of our #ICLR2023 workshop on "physics for ML" is already in one week ⚡ If the topic of your recent #ICML2023 submission is suitable for the workshop, you can submit it in parallel to the ICML conference track. Site: physics4ml.github.io

Due to the very high number of submissions for our #iclr2023 workshop on Physics for ML we are now in need of extra reviewers. Please message me / the other organisers if willing to review a couple of 4 page papers. You will have out eternal gratitude ❤️.

Big news! 🚨 Our workshop got accepted at ICML 2023! Join us to discuss how to combine scientific ⚗️ and ML 🤖 models into hybrid models 🧌. Let's bridge the gap between fields! 🤝 Stay tuned for more details 📻 #ICML2023

Lots of AI research focuses on massive un-curated data - what about highly curated data? Scientific models are just that: large amounts of carefully curated and summarized experimental data. But how can ML leverage them effectively? Join our ICML workshop: syns-ml.github.io/2023/

Antoine Wehenkel Emmanuel de Bézenac jörn jacobsen Anuj Karpatne Gilles Louppe Naoya Takeishi Please contribute to our workshop on the Synergy of Scientific and Machine Learning Modelling (SynS & ML 2023) by submitting your models, papers, and becoming reviewers! #ICML2023

Come join us at our #ICLR2023 workshop on "Physics for Machine Learning" #physics4ml🔥 Kicking off the workshop with the talk of Michael Bronstein "Physics inspired learning on Graphs"

📣 Join us for the Synergy of Scientific and Machine Learning Modeling workshop at #ICML2023! 🧪💻 Share your latest work on combining scientific and ML models and win awards sponsored by our partners. Submit contributions by May 24th AoE. For more info syns-ml.github.io/2023/

Reminder: Deadline for paper and scientific model tracks quickly approaching - 24th of May

A bit too tight for NeurIPS? 😵 Relax! 😎 You still have one week left to submit your great work that combines scientific models and machine learning to SynS & ML 2023! 🧪 + 🤖 = 💖 PS: this will give you a good excuse to visit Hawaï 🍹🏝️🌊

Very excited to announce that our entire ETH Zürich Deep Learning in Scientific Computing Master's course is now on YouTube! 📖 Prof. Siddhartha Mishra and I will talk you through PINNs, neural operators, neural ODEs, differentiable physics and more youtube.com/watch?v=y6wHpR…

Learning a lot about AI for physical sciences at the GAP workshop in Grenoble today ✅PINNs with Emmanuel de Bézenac ✅Neural ODEs & DL training with Julia Gusak gap2024.sciencesconf.org

Poseidon: Efficient Foundation Models for PDEs Exciting new multiscale operator transformer from ETH PDE folks: arXiv: arxiv.org/abs/2405.19101