HPC-Driven Modeling with ML-Based Surrogates for Magnon-Photon Dynamics in Hybrid Quantum System

We introduce a hybrid computational framework that merges HPC-based numerical solvers with physics-informed ML surrogates for efficient modeling of magnon-photon interactions. By running short-duration, high-fidelity Maxwell-LLG simulations and feeding their results into an ML model, we substantiall...

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Vydané v:2025 International Applied Computational Electromagnetics Society Symposium (ACES) s. 1
Hlavní autori: Song, Jialin, Tang, Yingheng, Ren, Pu, Takayoshi, Shintaro, Sawant, Saurabh, Zhu, Yujie, Hu, Jia-Mian, Nonaka, Andy, Mahoney, Michael W., Erichson, Benjamin, Yao, Zhi Jackie
Médium: Konferenčný príspevok..
Jazyk:English
Vydavateľské údaje: Applied Computational Electromagnetics Society 18.05.2025
Predmet:
Accuracy
Computational efficiency
Computational electromagnetics
Computational modeling
Numerical models
Predictive models
Quantum system
Spatiotemporal phenomena
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Popis
Shrnutí:We introduce a hybrid computational framework that merges HPC-based numerical solvers with physics-informed ML surrogates for efficient modeling of magnon-photon interactions. By running short-duration, high-fidelity Maxwell-LLG simulations and feeding their results into an ML model, we substantially cut simulation time while achieving accurate predictions across larger spatiotemporal domains.
DOI:10.23919/ACES66556.2025.11052493