PaddlePaddle/PaddleCFD
Apr 2025 – Jun 2025
3 Months active
Languages Used
MarkdownPythonXML
Technical Skills
Computational Fluid Dynamics (CFD)Deep LearningDocumentationPaddlePaddlePhysics-Informed Neural Networks (PINNs)Python
luzibo
PROFILE
Luzibo
Luzibo contributed to the PaddlePaddle/PaddleCFD repository by developing end-to-end neural network pipelines for computational fluid dynamics, focusing on physics-informed approaches. Over three months, Luzibo implemented a lid-driven cavity flow simulation using Physics-Informed Neural Networks with PirateNets and SOAP optimizers, replacing legacy workflows and providing reproducible benchmarking at high Reynolds numbers. They also integrated GreenSONet and Green-ONet models for 3D Stokes flow, updating simulation data and VTK support for advanced testing. Luzibo enhanced onboarding and cross-team collaboration by improving documentation, standardizing mathematical notation, and clarifying workflows. Their work demonstrated depth in Python, deep learning, and scientific computing.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
12Total
Bugs
0
Commits
12
Features
4
Lines of code
422,790
Activity Months3
Your Network
109 people
Work History
June 2025
2 Commits • 1 Features
Jun 1, 2025June 2025 PaddleCFD monthly summary: Delivered end-to-end neural network modeling support for 3D Stokes flow via GreenSONet and Green-ONet, advancing PaddleCFD's physics-informed PDE capabilities. Key focus was integrating GreenSONet/Green-ONet with existing simulation workflows, including updates to simulation data/configs and VTK-related changes to support 3D lid-driven cavity tests. Established robust pipelines for inference, testing, data loading, mesh handling, and evaluation to accelerate validation and deployment of neural solvers.
2 Commits • 1 Features
Jun 1, 2025June 2025 PaddleCFD monthly summary: Delivered end-to-end neural network modeling support for 3D Stokes flow via GreenSONet and Green-ONet, advancing PaddleCFD's physics-informed PDE capabilities. Key focus was integrating GreenSONet/Green-ONet with existing simulation workflows, including updates to simulation data/configs and VTK-related changes to support 3D lid-driven cavity tests. Established robust pipelines for inference, testing, data loading, mesh handling, and evaluation to accelerate validation and deployment of neural solvers.
June 2025
May 2025
8 Commits • 2 Features
May 1, 2025May 2025 monthly summary for PaddlePaddle/PaddleCFD focusing on documentation improvements to accelerate onboarding, reduce support overhead, and align with project standards. Delivered two major README enhancements: PPDeepONet README improvements and LDC example README in PaddleCFD. Documentation updates corrected pretrained model path references, clarified mathematical equations, standardized boundary condition formatting, and improved overall README consistency. The work establishes a robust documentation baseline for future feature work and cross-team contributions, enabling faster adoption of the PPDeepONet workflow in CFD contexts.
May 2025
8 Commits • 2 Features
May 1, 2025May 2025 monthly summary for PaddlePaddle/PaddleCFD focusing on documentation improvements to accelerate onboarding, reduce support overhead, and align with project standards. Delivered two major README enhancements: PPDeepONet README improvements and LDC example README in PaddleCFD. Documentation updates corrected pretrained model path references, clarified mathematical equations, standardized boundary condition formatting, and improved overall README consistency. The work establishes a robust documentation baseline for future feature work and cross-team contributions, enabling faster adoption of the PPDeepONet workflow in CFD contexts.
April 2025
2 Commits • 1 Features
Apr 1, 2025Summary for 2025-04: Delivered end-to-end Lid-Driven Cavity (LDC) flow simulation pipeline using Physics-Informed Neural Networks (PINNs) with PirateNets/SOAP in PaddleCFD, enabling reproducible benchmarking at Re=3200. Replaced legacy MLP example with the LDC workflow and added training/evaluation/export/inference scripts and a comprehensive README detailing the problem setup and the PirateNets/SOAP approach. This work is supported by two commits: 254d3789608b67a30fe31ba282f9358f910d2f3d (add ldc code) and d07e00ce4ea4ed51873012278a9ffb0b9d4a8c14 (Add model architecture diagram).
2 Commits • 1 Features
Apr 1, 2025Summary for 2025-04: Delivered end-to-end Lid-Driven Cavity (LDC) flow simulation pipeline using Physics-Informed Neural Networks (PINNs) with PirateNets/SOAP in PaddleCFD, enabling reproducible benchmarking at Re=3200. Replaced legacy MLP example with the LDC workflow and added training/evaluation/export/inference scripts and a comprehensive README detailing the problem setup and the PirateNets/SOAP approach. This work is supported by two commits: 254d3789608b67a30fe31ba282f9358f910d2f3d (add ldc code) and d07e00ce4ea4ed51873012278a9ffb0b9d4a8c14 (Add model architecture diagram).
April 2025
Activity
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Quality Metrics
Correctness94.2%
Maintainability93.4%
Architecture94.2%
Performance93.4%
AI Usage20.0%
Skills & Technologies
Programming Languages
MarkdownPythonXML
Technical Skills
Computational Fluid Dynamics (CFD)Data ProcessingDeep LearningDocumentationNumerical MethodsPaddlePaddlePhysics-Informed Neural Networks (PINNs)PythonScientific ComputingTechnical Writing
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