A Differentiable Framework for Full and Phaseless Data Inversion Using Neural Implicit Contrast-Source Representation
arXiv:2508.10555v2 Announce Type: replace-cross Abstract: In this study, we extend the contrast source inversion to a fully differentiable, unsupervised framework based on a neural implicit representation of the contrast source. Specifically, instead of a pixel-wise discrete representation, the contrast source is parameterized by a lightweight residual multilayer perceptron (ResMLP) as a continuous neural field conditioned on spatial coordinates and transmitter settings. This continuous parameterization provides a more flexible representation of the contrast source and improves reconstruction
The paper was just published on arXiv, signaling a new advancement in AI-driven inversion techniques leveraging neural implicit representations.
This research provides a more flexible and potentially more accurate method for solving inverse problems in various scientific and engineering domains, typically done in physics, which AI could make more efficient.
Traditional pixel-wise discrete representations are being replaced by continuous neural implicit representations, offering improved reconstruction and potentially faster, more efficient data inversion.
- · AI/ML researchers
- · Physics-based simulation companies
- · Medical imaging sector
- · Geophysical exploration firms
- · Traditional inversion algorithm developers
- · Firms relying on less efficient discrete methods
More accurate and faster inversion of complex data sets in scientific and engineering fields.
Reduced computational costs and accelerated discovery or development cycles in areas like materials science or medical diagnostics.
Enhanced industrial competitiveness for nations and companies adopting these advanced AI-driven simulation techniques.
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Read at arXiv cs.LG