PaddlePaddle/PaddleCFD
Mar 2025 – Jul 2025
4 Months active
Languages Used
PythonC++MarkdownShellYAML
Technical Skills
Deep LearningModel ImplementationNeural NetworksNumerical MethodsPaddlePaddlePython
guhaohao0991
PROFILE
Guhaohao0991
Over four months, this developer contributed to PaddlePaddle/PaddleCFD by building advanced machine learning workflows for scientific computing. They implemented Kolmogorov-Arnold Network operators and integrated KANONet and DeepONet architectures, enabling flexible, physics-informed PDE modeling in Python. Their work included developing data conversion scripts to transform CFD mesh and flow-field data into tensor-ready formats, streamlining ML integration. They enhanced airfoil and Darcy flow simulation examples, reorganized file structures for maintainability, and improved documentation for user onboarding. Using Python, YAML, and C++, they focused on code quality, configuration management, and robust data preprocessing, demonstrating depth in both model implementation and workflow engineering.
Overall Statistics
Feature vs Bugs
89%Features
Repository Contributions
23Total
Bugs
1
Commits
23
Features
8
Lines of code
6,175
Activity Months4
Your Network
9 people
Work History
July 2025
16 Commits • 3 Features
Jul 1, 2025During July 2025, PaddleCFD delivered targeted improvements to user documentation, expanded ML-driven modeling capabilities, and hardened example workflows. Documentation quality across aerodynamics, Darcy flow, and PP-KAN examples was enhanced for readability and consistency, including corrected math rendering and unified presentation. The team added KANONet/DeepONet support to DarcyFlow workflows, refreshed input handling, and updated training commands and the PP-KAN environment to streamline experimentation. Data-path and configuration handling for DarcyFlow and PP-KAN were fixed to ensure reliable dataset access and reduce breakages. A light cleanup of tests and formatting improved code quality without changing behavior. These changes collectively improve user onboarding, reduce time-to-value, and enable more robust ML experiments in CFD workflows.
16 Commits • 3 Features
Jul 1, 2025During July 2025, PaddleCFD delivered targeted improvements to user documentation, expanded ML-driven modeling capabilities, and hardened example workflows. Documentation quality across aerodynamics, Darcy flow, and PP-KAN examples was enhanced for readability and consistency, including corrected math rendering and unified presentation. The team added KANONet/DeepONet support to DarcyFlow workflows, refreshed input handling, and updated training commands and the PP-KAN environment to streamline experimentation. Data-path and configuration handling for DarcyFlow and PP-KAN were fixed to ensure reliable dataset access and reduce breakages. A light cleanup of tests and formatting improved code quality without changing behavior. These changes collectively improve user onboarding, reduce time-to-value, and enable more robust ML experiments in CFD workflows.
July 2025
June 2025
4 Commits • 3 Features
Jun 1, 2025June 2025 monthly summary for PaddleCFD focusing on feature delivery, maintainability, and onboarding improvements within the PaddlePaddle/PaddleCFD repository.
June 2025
4 Commits • 3 Features
Jun 1, 2025June 2025 monthly summary for PaddleCFD focusing on feature delivery, maintainability, and onboarding improvements within the PaddlePaddle/PaddleCFD repository.
April 2025
1 Commits • 1 Features
Apr 1, 2025April 2025: Delivered an end-to-end CFD data preparation enhancement for PaddleCFD by introducing the su2_vtk2npy.py script that converts SU2 mesh and VTK flow-field data into a graph-ready format for machine learning. This suite produces tensor-ready representations with node coordinates, edge connectivity, and feature keys/values, enabling seamless ML integration and faster experimentation with CFD data. The work is backed by the commit 'add su2 converter' (b0769f08d197c701065050b45dbaf254e756f119). No major bug fixes were recorded this month.
1 Commits • 1 Features
Apr 1, 2025April 2025: Delivered an end-to-end CFD data preparation enhancement for PaddleCFD by introducing the su2_vtk2npy.py script that converts SU2 mesh and VTK flow-field data into a graph-ready format for machine learning. This suite produces tensor-ready representations with node coordinates, edge connectivity, and feature keys/values, enabling seamless ML integration and faster experimentation with CFD data. The work is backed by the commit 'add su2 converter' (b0769f08d197c701065050b45dbaf254e756f119). No major bug fixes were recorded this month.
April 2025
March 2025
2 Commits • 1 Features
Mar 1, 2025Concise monthly summary for 2025-03 focused on delivering a major operator-level expansion for PDE solving within PaddleCFD. Implemented the Kolmogorov-Arnold Network (KAN) operator and supporting architectures to enable flexible, physics-informed modeling directly in PaddlePaddle.
March 2025
2 Commits • 1 Features
Mar 1, 2025Concise monthly summary for 2025-03 focused on delivering a major operator-level expansion for PDE solving within PaddleCFD. Implemented the Kolmogorov-Arnold Network (KAN) operator and supporting architectures to enable flexible, physics-informed modeling directly in PaddlePaddle.
Activity
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Quality Metrics
Correctness89.6%
Maintainability88.6%
Architecture86.0%
Performance82.6%
AI Usage20.0%
Skills & Technologies
Programming Languages
C++MarkdownPythonShellYAML
Technical Skills
Bug FixingCFDCode CleanupCode OrganizationCode RefactoringConfiguration ManagementData ConversionData PreprocessingData ProcessingData StructuresData VisualizationDeep LearningDependency ManagementDocumentationDocumentation Update
Repositories Contributed To
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