Rewriting Plasma Physics with Differentiable Programming
![Differentiable programming enables computational workflows that progress from manual iteration and costly parameter scans-scaling at [latex]\mathcal{O}(k^{N})[/latex]-to gradient-based optimization via reverse-mode automatic differentiation and, ultimately, function learning through neural networks embedded within differentiable solvers, representing the core focus of this work.](https://arxiv.org/html/2603.11231v1/x1.png)
A new approach leveraging automatic differentiation is transforming how we analyze, optimize, and design plasma systems.
![Differentiable programming enables computational workflows that progress from manual iteration and costly parameter scans-scaling at [latex]\mathcal{O}(k^{N})[/latex]-to gradient-based optimization via reverse-mode automatic differentiation and, ultimately, function learning through neural networks embedded within differentiable solvers, representing the core focus of this work.](https://arxiv.org/html/2603.11231v1/x1.png)
A new approach leveraging automatic differentiation is transforming how we analyze, optimize, and design plasma systems.

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