Multimodal super-resolution: discovering hidden physics and its application to fusion plasmas.

ABSTRACT: Understanding complex physical systems often requires integrating data from multiple diagnostics, each with limited resolution or coverage. We present a machine learning framework that reconstructs synthetic high-temporal-resolution data for a target diagnostic using information from other diagnostics, without direct target measurements during the inference. This multimodal super-resolution technique improves diagnostic robustness and enables monitoring even in case of measurement failures or degradation. Applied to fusion plasmas, our method targets edge-localized modes (ELMs), which can damage plasma-facing materials. By reconstructing super-resolution Thomson Scattering data from complementary diagnostics, we uncover fine-scale plasma dynamics and validate the role of resonant magnetic

SUBMITTER: Jalalvand A 

PROVIDER: S-EPMC12475251 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature