Space–Time Adaptive Modeling and Shape Optimization of Microwave Structures With Applications to Metasurface Design
Bibliographic record
Abstract
This article presents a time-domain modeling and shape optimization framework for microwave structures, including metasurfaces, based on a nodal discontinuous Galerkin time-domain (DGTD) method. In particular, we employ an unstructured mesh and the inherent mesh refinement ability of DGTD to model multiscale geometries via adaptive mesh refinement. More importantly, we integrate a multitier local time-stepping technique into the time integration of DGTD, which significantly alleviates the cost introduced by mesh refinement. We further present a flexible parameterization approach by defining the contours of metasurface unit cells by B-spline curves, which allows us to explore a wide range of smooth shapes by only a few design variables. Besides, we adopt a polynomial chaos-Kriging (PCK) surrogate method to approximate the full-wave DGTD model, reducing the computational cost for optimization problems that require time-consuming simulations by three orders of magnitude. The B-spline parameterization and the PCK surrogate model are combined with a pattern search-based Pareto front algorithm to optimize metasurfaces for multiple design objectives. The proposed optimization framework is also applicable to other microwave structures. We demonstrate the effectiveness of our approach through the optimization of an omega-bianisotropic Huygens’ metasurface unit cell and an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$E$ </tex-math></inline-formula> -plane microwave T-junction.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".