PaddlePaddle/PaddleCFD
Dec 2025 – Dec 2025
1 Month active
Languages Used
Python
Technical Skills
PaddlePaddleconfiguration managementdata processingdeep learningfluid dynamicslogging
ADream-ki
PROFILE
Adream-ki
During December 2025, this developer delivered the consolidated CoNFiLD feature for the PaddlePaddle/PaddleCFD repository, focusing on spatiotemporal turbulence field generation and symbolic dynamics discovery. They implemented the CoNFiLD model using Python and PaddlePaddle, integrating deep learning techniques for data-driven equation discovery in fluid dynamics. Their work included enhancements to configuration management and logging, enabling reproducible research workflows and easier user adoption. Comprehensive documentation and improved dataset structures were provided to support usability. The depth of the engineering effort is reflected in robust configuration, prompt-logging optimizations, and a focus on stability, addressing both technical and user-facing requirements.
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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