PaddlePaddle/PaddleCFD
Dec 2025 – Dec 2025
1 Month active
Languages Used
Python
Technical Skills
PaddlePaddleconfiguration managementdata processingdeep learningfluid dynamicslogging
ADream-ki
PROFILE
Adream-ki
Developed and delivered the consolidated CoNFiLD feature for the PaddlePaddle/PaddleCFD repository, focusing on spatiotemporal turbulence field generation and symbolic dynamics discovery. Leveraged Python and PaddlePaddle to implement a deep learning model that enables reproducible generation of turbulent flow fields and data-driven equation discovery for fluid dynamics research. Enhanced configuration management and logging systems to streamline user workflows, improve stability, and facilitate easier adoption. Added comprehensive user-facing documentation and configuration enhancements, covering dataset structures and parameterization. Optimized prompt logging and formatting, addressing minor bugs to ensure a robust, maintainable codebase that accelerates research and supports advanced model training scenarios.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
2Total
Bugs
0
Commits
2
Features
1
Lines of code
11,330
Activity Months1
Your Network
9 peopleWork History
December 2025
2 Commits • 1 Features
Dec 1, 2025Monthly summary for 2025-12: Delivered the consolidated CoNFiLD feature for PaddleCFD, including the Spatiotemporal Turbulence Field Generator and Symbolic Dynamics Discovery, plus user-facing docs and configuration enhancements. Major bugs fixed and prompt-logging optimizations to improve stability and usability. Overall, this work accelerates research workflows by enabling reproducible turbulence field generation and data-driven equation discovery, with robust configuration and logging for easier adoption.
2 Commits • 1 Features
Dec 1, 2025Monthly summary for 2025-12: Delivered the consolidated CoNFiLD feature for PaddleCFD, including the Spatiotemporal Turbulence Field Generator and Symbolic Dynamics Discovery, plus user-facing docs and configuration enhancements. Major bugs fixed and prompt-logging optimizations to improve stability and usability. Overall, this work accelerates research workflows by enabling reproducible turbulence field generation and data-driven equation discovery, with robust configuration and logging for easier adoption.
December 2025
Activity
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Quality Metrics
Correctness80.0%
Maintainability80.0%
Architecture80.0%
Performance80.0%
AI Usage50.0%
Skills & Technologies
Programming Languages
Python
Technical Skills
PaddlePaddleconfiguration managementdata processingdeep learningfluid dynamicsloggingmodel trainingsymbolic regression
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