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New FDF framework simplifies AI-driven digital twin development

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new framework called Function+Data Flow (FDF) to streamline the creation of digital twins that utilize AI and machine learning pipelines. Implemented within the DesCartes Builder environment, FDF aims to make these complex pipelines more accessible and reliable, particularly for domain experts. An empirical user study indicated good usability for the tool, and based on feedback, an extended Hierarchical FDF (H-FDF) has been proposed to support more complex, iterative pipeline structures. AI

IMPACT Simplifies the engineering of AI-based digital twins, potentially making them more accessible to domain experts.

RANK_REASON The cluster contains an academic paper detailing a new framework and its evaluation. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New FDF framework simplifies AI-driven digital twin development

COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Eduardo de Conto, Blaise Genest, Arvind Easwaran, Nicholas Ng, Shweta Menon ·

    Building real-time digital twin instances with Function+Data Flow: user evaluation and extension for iterative pipelines

    arXiv:2608.18480v1 Announce Type: cross Abstract: Digital twins (DTs) increasingly leverage artificial intelligence (AI) and machine learning (ML) pipelines, both to build real-time DTs from high-fidelity simulations and to instantiate them with historical data. However, engineer…

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