Efficient Data-Driven Simulation of Microwave Interaction With Complex Plasma Profiles
Abstrak
Microwave interactions reveal electromagnetic properties of complex materials like plasma that aid in under-standing plasma’s wide range of applications in manufacturing, aerospace, and more. The interactions generally comprise transmission, absorption, and scattering of microwaves with the dense plasma profile. Traditional computational methods face a challenge in simulating the complex interaction between the microwave and asymmetric plasma profiles due to the trade-off between the number of computations and the accuracy of the result, which encourages exploring other techniques. Data-driven deep learning techniques due to their ability to decipher hidden pattern from input data finds application in solving various advanced scientific/engineering problems. The technique has been applied to investigate complex microwave plasma interactions. The proposed deep learning model, trained on different asymmetric plasma profiles and corresponding scattered microwave E-field patterns, achieved 300-500 times faster and accurate predictions that are within acceptable limits. Thus, affirms the model’s applicability in accelerating future studies on plasma dynamics.
Penulis (3)
Pratik Ghosh
B. Chaudhury
Shishir Purohit
Akses Cepat
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Cek di sumber asli →- Tahun Terbit
- 2024
- Bahasa
- en
- Total Sitasi
- 1×
- Sumber Database
- Semantic Scholar
- DOI
- 10.1109/MAPCON61407.2024.10923364
- Akses
- Open Access ✓