Felix Koehler @felix_m_koehler
🤖 Machine Learning & 🌊 Simulation | 📺 YouTuber | 🧑🎓 PhD student @thuereyGroup https://t.co/tmQfEQ1li4 fkoehler.site Munich Joined July 2021-
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📢 Calling for (4-page) workshop papers on #differentiable programming and #SciML. This is a great opportunity to showcase your work or work in progress alongside keynote presentations by Petros Koumoutsakos, @PatrickKidger, @thuereyGroup & Astrid Walle.
📢 Calling for (4-page) workshop papers on #differentiable programming and #SciML. This is a great opportunity to showcase your work or work in progress alongside keynote presentations by Petros Koumoutsakos, @PatrickKidger, @thuereyGroup & Astrid Walle.
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Felix Koehler retweeted
🚨🚨🚨 Call for papers alert 🚨🚨🚨 The "Differentiable Systems and Scientific Machine Learning" workshop at the 1st EurIPS conference is now accepting submissions! Let's explore the intersection of differentiable programming and SciML together. differentiable-systems.github.io/workshop-eurip…
Felix Koehler retweeted
Journal of Open Source Software just published our "Tesseract Core: Universal, autodiff-native software components for Simulation Intelligence", @dionhaefner @AlexLavin_C137 🎉 doi.org/10.21105/joss.…
Amazing event for people at ICML. Sad I cannot attend.
Amazing event for people at ICML. Sad I cannot attend.
This week I started as a Research Scientist Intern at Meta Reality Labs in wonderful Zurich, Switzerland🇨🇭. Let me know if you are around and want to chat about neural emulators, differentiable physics, numerical solvers, etc.
Felix Koehler retweeted
I wanted to highlight PBDL's brand-new sections on diffusion models with code and derivations! Great work by Benjamin Holzschuh, with neat Jupyter notebooks 👍 All the way from normalizing flow basics over score matching to denoising & flow matching. E.g. colab.research.google.com/github/tum-pbs…
Felix Koehler retweeted
What is the role of memory for modeling time dependent PDEs? I will be at ICLR presenting our paper (Oral) where we study when it is beneficial for modeling time-dependent PDEs! 🔗openreview.net/forum?id=o9kqa… [Oral]: Thu 24 Apr 10:30 am @ Session 1E [Poster]: Thu 24 Apr 3 pm #617
Felix Koehler retweeted
At #ICLR2025 , check out our poster today on forecasting chaos with foundation models. @YuanzhaoZhang Poster #43. Thu 24 Apr, 3 - 5:30 pm in Hall 3 + Hall 2B
Felix Koehler retweeted
If you are at ICLR and interested in ways to make denoising diffusion more efficient, please come to Vinh’s oral talk tomorrow at 11:30 in oral session 1C. It also involves backprop through ODE solvers and constrained learning.
If you are at ICLR and interested in ways to make denoising diffusion more efficient, please come to Vinh’s oral talk tomorrow at 11:30 in oral session 1C. It also involves backprop through ODE solvers and constrained learning. https://t.co/N41SORCDY7
Kanishk and I will be presenting PRDP on Saturday at 3pm at poster nr. 538.
Kanishk and I will be presenting PRDP on Saturday at 3pm at poster nr. 538.
Felix Koehler retweeted
If you're at #ICLR 2025 in Singapore, please check out our posters 🤗 I'm sure it's going to be a great conference! Have fun everyone...
Travelling to Singapore next week for #ICLR2025 presenting this paper (Sat 3 pm nr. 538): arxiv.org/abs/2502.19611 DM me (Whova, Email or X) if you want to chat about (autoregressive) neural emulators/operators for PDE, autodiff, differentiable physics, numerical solvers etc. 😊
Felix Koehler retweeted
Just found a completely free Computational Fluid Dynamics Course Module by @MathWorks !!! --> github.com/MathWorks-Teac… - enjoy ;-)
Today, I had the chance to present my #NeurIPS paper "APEBench" @SimAI4Science . You can find the recording on YouTube: youtu.be/wie-SzD6AJE
Felix Koehler retweeted
Our "Diffusion Graph Nets" paper has been accepted for Oral at #ICLR2025 🎉 DGNs are graph-based latent diffusion models that sample fully-developed fluid flows. Key strengths: ✅ Fast, accurate flow stats ✅ Works on unstructured meshes ✅ Learns from short simulations
I created a video to help you get started using the APEBench suite (my recent #neurips paper) to benchmark autoregressive neural emulators for PDEs with a simple ConvNet emulation of 1D advection: youtu.be/q8fjQ4ZFynw
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