Fast approximate solvers for metamaterials design in electromagnetism
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arXiv
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866909784968527872 |
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| author | Pestourie, Raphael |
| author_facet | Pestourie, Raphael |
| contents | In electromagnetism, the model of Maxwell's equations yields accurate and trustworthy predictions. Numerical solvers can reach electromagnetic solutions far beyond the set of analytical closed-form solutions; this is crucial in metamaterials design where the goal is to find the geometry that generates an optimal electromagnetic solution for a desired property. Then why do we still need to create computational methodologies in metamaterials research? And why should we care about analytical models? The simulation and optimization bottlenecks limit the range of applications of the state of the art. Hence, there is room for opportunities to create new computational methodologies. Semi-analytical methods that are customized for classes of applications enable simulation and optimization. We detail the state of the research in customized methodologies using the illustrative example of optical metasurfaces and suggest best practices. We also discuss how machine learning may enhance these methodologies. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_20065 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Fast approximate solvers for metamaterials design in electromagnetism Pestourie, Raphael Applied Physics Optics In electromagnetism, the model of Maxwell's equations yields accurate and trustworthy predictions. Numerical solvers can reach electromagnetic solutions far beyond the set of analytical closed-form solutions; this is crucial in metamaterials design where the goal is to find the geometry that generates an optimal electromagnetic solution for a desired property. Then why do we still need to create computational methodologies in metamaterials research? And why should we care about analytical models? The simulation and optimization bottlenecks limit the range of applications of the state of the art. Hence, there is room for opportunities to create new computational methodologies. Semi-analytical methods that are customized for classes of applications enable simulation and optimization. We detail the state of the research in customized methodologies using the illustrative example of optical metasurfaces and suggest best practices. We also discuss how machine learning may enhance these methodologies. |
| title | Fast approximate solvers for metamaterials design in electromagnetism |
| topic | Applied Physics Optics |
| url | https://arxiv.org/abs/2503.20065 |