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Neural adjoint method
Neural adjoint
Introduced by Simiao Ren et al. in Benchmarking deep inverse models over time, and the neural-adjoint method
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
The NA method can be divided into two steps: (i) Training a neural network approximation of f , and (ii) inference of xˆ. Step (i) is conventional and involves training a generic neural network on a dataset ˆ of input/output pairs from the simulator, denoted D, resulting in f, an approximation of the forward ˆ model. This is illustrated in the left inset of Fig 1. In step (ii), our goal is to use ∂f/∂x to help us gradually adjust x so that we achieve a desired output of the forward model, y. This is similar to many classical inverse modeling approaches, such as the popular Adjoint method [8, 9]. For many practical ˆ expression for the simulator, from which it is trivial to compute ∂f/∂x, and furthermore, we can use modern deep learning software packages to efficiently estimate gradients, given a loss function L. More formally, let y be our target output, and let xˆi be our current estimate of the solution, where i indexes each solution we obtain in an iterative gradient-based estimation procedure. Then we compute xˆi+1 with inverse problems, however, obtaining ∂f/∂x requires significant expertise and/or effort, making these approaches challenging. Crucially, fˆ from step (i) provides us with a closed-form differentiable
Papers archive 2025-07-28
3 shown of 3, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.
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Enhancing Inverse Problem Solutions with Accurate Surrogate Simulators and Promising Candidates 26 Apr 2023 · 1 repository · arXiv:2304.13860
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Inverse deep learning methods and benchmarks for artificial electromagnetic material design 19 Dec 2021 · 2 repositories · arXiv:2112.10254
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Benchmarking deep inverse models over time, and the neural-adjoint method 27 Sep 2020 · 1 repository · arXiv:2009.12919Syntology ran 2 of 2 samples · 0 unverified · 2 pointer-only (licence)
Tasks archive 2025-07-28
2 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.
| Task | Papers |
|---|---|
| Benchmarking | 1 |
| Robust Design | 1 |
Usage over time archive 2025-07-28
Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).
Categories archive 2025-07-28
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