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Articole: 5

  1. Hybridization of Neural Networks and Numerical Solvers in JAX with Differentiable Physics (Lecture and Practical Workshop)

    (Felix Köhler’s Website)

    I was invited to give a lecture and practical sessions at the Workshop on Machine Learning and Automatic Differentiation in JAX for Scientific Computing hosted by the University of Strasbourg in June 2026.

  2. From Numerical Simulators to Neural Emulators and Back (Talk at RISE ML Seminar)

    (Felix Köhler’s Website)

    I had the honor to be invited to the RISE ML seminar series and speak about my current research.

  3. My NeurIPS 2024 Paper: APEBench

    (Felix Köhler’s Website)

    🎉 I am happy to announce NeurIPS2024 paper: APEBench. 🧵 Check out the project page 👉 Code 🤖 Or install via pip (requires JAX): pip install apebench In this Bluesky thread, I summarize APEBench’s main contributions.

  4. Lecture: Automatic Differentiation & Adjoint Methods in Differentiable Physics

    (Felix Köhler’s Website)

    As part of our master course in Advanced Deep Learning for Physics (IN2298), I gave a lecture on autodiff and adjoint methods.

  5. My new Website and Blog

    (Felix Köhler’s Website)

    This is my new website and blog.