arXiv AI
Simulator-Refined Diffusion for Radio-Frequency Inverse Design
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arXiv Machine Learning
Sep 22
K-TRAIL: Simulator-Guided Generative Design of EM/RF Circuits
K-TRAIL is a simulator-guided generative framework that uses diffusion-based layout generation combined with derivative-free ensemble Kalman guidance to design RF and electromagnetic circuits. It allows a black-box EM simulator to refine candidate layouts during generation, supporting synthesis from target S-parameter responses or direct RF performance constraints. Experiments on multi-layer RFIC structures demonstrate improved agreement with target responses and the ability to discover structurally distinct layouts that meet design requirements.
arXiv Machine Learning
Aug 21
Guided Diffusion by Optimized Loss Functions on Relaxed Parameters for Inverse Material Design
arXiv:2602. 15648v2 Announce Type: replace Abstract: Inverse design problems are common in engineering and materials science.
arXiv Machine Learning
Sep 1
Diffusion-Based Inverse Design of Dielectric Resonator Metasurfaces for Shaping Smart Electromagnetic Environments
The paper presents a conditional diffusion framework for the inverse design of dielectric resonator metasurfaces based on target angular scattering patterns. Trained on T‑matrix simulated geometry‑response pairs, the model learns a distribution of feasible geometries, allowing multiple candidate designs for the ill‑posed inverse problem. The best generated metasurface achieves a mean percentage error of 1.39%, outperforming CMA‑ES optimization and deterministic neural baselines, and can be inferred in about one minute after training.
arXiv Machine Learning
Jun 30
PCGD: Physics-Guided Conditional Graph Diffusion for TCAD Device Simulation
arXiv:2606. 29272v1 Announce Type: new Abstract: Technology computer-aided design (TCAD) semiconductor device simulation is fundamentally constrained by the high computational cost of iteratively solving coupled drift-diffusion equations.
arXiv Machine Learning
Sep 15
Benchmarking Optimizers to Solve Inverse Problems with Differentiable Physics Simulators
arXiv:2609.13819v1 Announce Type: new Abstract: Solving inverse problems with differentiable physics simulators holds the potential to revolutionize scientific discovery and engineering design, as it...
arXiv Machine Learning
4d ago
Optimization Using Pathwise Algorithmic Derivatives of Electromagnetic Shower Simulations
arXiv:2405.07944v1 Announce Type: cross Abstract: Among the well-known methods to approximate derivatives of expectancies computed by Monte-Carlo simulations, averages of pathwise derivatives are oft...