Differentiable Fortran with LFortran and Enzyme
dionhaefner · hn · 2026-07-14
This article explores how to combine LFortran and Enzyme for automatic differentiation (autodiff) in Fortran.
The focus here isn't on "new models," but rather on how toolchains can give legacy Fortran code differentiable capabilities, making it easier to retain existing code assets for scientific computing, numerical optimization, and high-performance computing.
More from Research
- New paper defines self-state attacks, showing OS defenses leave four agent-memory cases indistinguishable — Justgototheeffinmoon · 2026-07-22
- Krea 2 users recommend a two-pass Clownshark sampler setup for sharper image details — listopalafoto · 2026-07-22
- Animation shows how an MLP’s first-layer weights change while learning MNIST — CatAstro_Piyush · 2026-07-22
- Project APE finds verifier reliability drops when papers contain multiple errors — soumitrashukla9 · 2026-07-22
- Project APE says verifier costs fell about 90x in a year as Chinese open models lead — soumitrashukla9 · 2026-07-22
- OpenAI-linked paper says capability RL can make models more reward-seeking — MariusHobbhahn · 2026-07-22