MODular Analysis for Multimodal Intelligent Applications

The fusion energy sector is at a pivotal stage where large volumes of experimental and simulation data are available but underutilized due to the lack of efficient tools for machine learning (ML) and artificial intelligence (AI) workflows. Existing efforts such as the Fusion Data Platform (FDP) and MGKDB have improved data accessibility, but preparing this data for AI/ML tasks remains labor-intensive. MODALIA (MODular Analysis for Multimodal Intelligent Applications) is designed to address this bottleneck by automating data curation, labeling, and analysis. This platform will accelerate the development of predictive, diagnostic, and simulation capabilities, ultimately enhancing the fusion community's capacity to harness data for ML/AI-based research and real-world applications. MODALIA consists of a series of advanced modules including a multimodal Data Labeler, Prospector for data mining, a Spectroscopy module, and a System Identification and Regression (SIR) module. These tools will be equipped with capabilities such as automated Bayesian inference, hyperparameter optimization, adaptive mesh refinement, and Gaussian Process Regression (GPR). These tools are tailored to the needs of plasma physicists and fusion application scientists to improve the efficiency of data preparation and analysis, enhancing simulation accuracy and experimental data processing. The platform will be implemented as a web-based application, unifying several core tools and simplifying the user interface to improve accessibility. MODALIA’s architecture will enable seamless integration of various data formats and sources, support signal smoothing and interpolation for time-series data, and provide visualization tools for validating transformations prior to analysis. In addition to streamlining ML/AI workflows, MODALIA will ensure data privacy and integrity through robust security protocols, making it suitable for both academic research and commercial fusion projects. By providing a centralized platform for advanced data analysis and ML integration, MODALIA will significantly accelerate fusion energy research. The tools will improve predictive capabilities, reduce development time, and provide real-time insights into plasma behavior, especially for high-value discharges. The commercialization of MODALIA will provide essential tools for private fusion energy companies, enabling them to optimize reactor designs and reduce operational risks while maintaining the confidentiality of proprietary data. In the broader context, MODALIA will contribute to the development of fusion as a clean, sustainable energy source by improving the efficiency and effectiveness of research in this critical area. Additionally, the project will foster job creation in the fields of AI, data science, and plasma physics, and support the development of a skilled workforce proficient in these technologies.