kailaix/ADCME.jl
Automatic Differentiation Library for Computational and Mathematical Engineering
This project helps scientists and engineers solve complex inverse modeling problems using gradient-based optimization. You provide observational data and a mathematical model of your system (like a physics simulation), and it outputs the unknown parameters or functions within your model that best explain the observed data. It's designed for researchers and practitioners in fields such as geophysics, fluid dynamics, and solid mechanics.
317 stars. No commits in the last 6 months.
Use this if you need to determine unknown causes or properties of a system from its observed effects, especially in scientific and engineering simulations.
Not ideal if your problem does not involve differential equations, numerical simulations, or requires symbolic differentiation instead of automatic differentiation.
Stars
317
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62
Language
Julia
License
MIT
Category
Last pushed
Oct 18, 2023
Commits (30d)
0
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