# Physics-enhanced deep surrogates for partial differential equations

**URL:** <https://discourse.julialang.org/t/physics-enhanced-deep-surrogates-for-partial-differential-equations/108596>\
**Category:** Machine Learning\
**Created:** [January 9, 2024, 11:03pm UTC](https://discourse.julialang.org/t/physics-enhanced-deep-surrogates-for-partial-differential-equations/108596 "2024-01-09T23:03:50Z")\
**Posts on this page:** 1\
**Page:** 1

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**Author:** ![Perrin\_Meyer](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/perrin_meyer/32/15686_2.png) [@Perrin\_Meyer](https://discourse.julialang.org/u/Perrin_Meyer)\
**Post date:** [January 9, 2024, 11:03pm UTC](https://discourse.julialang.org/t/physics-enhanced-deep-surrogates-for-partial-differential-equations/108596/1 "2024-01-09T23:03:50Z")

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Very Interesting new Nature ML paper from some of the core Julia developers (using Julia as well).

> **[Technique could efficiently solve partial differential equations for numerous...](https://news.mit.edu/2024/peds-technique-could-efficiently-solve-partial-differential-equations-0108)**
>
> MIT researchers propose a "physics-enhanced deep-surrogate" (PEDS) method for developing data-driven surrogate models for complex physical systems in such fields as mechanics, optics, thermal transport, fluid dynamics, physical chemistry, and climate...

(It’s nice the journal article is open access, it’s worth a read).