arXiv (All)
open access
Multi-Level-Set-Based Physics-Driven Neural Network to Solve 3-D Inverse Scattering Problems
This paper proposes a level-set-based physics-driven neural network solver (LSPDNN) for 3-D electromagnetic inverse scattering. To mitigate boundary blurring and reconstruction artifacts in voxel-wise contrast reconstruction, the proposed solver exploits the piecewise homogeneity of practical scatte
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- A Model-Informed Deep Learning Algorithm for Solving the Phaseless Inverse Scattering Problem#1117782
- Unsupervised deep learning for inverse problems in computed tomography.#1166972
- Dimension Bridging for 3D RANS with Neural Network Accelerated Gaussian Functional Regression#780940
- Dimension Bridging for 3D RANS with Neural Network Accelerated Gaussian Functional Regression#779565
- Co-PiLOT: Constrained Physics-Informed Latent Optimization for Target-Driven Inverse Design#1142182
Record · ID 985934
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive