SwanLab/Swan
Nov 2024 – Aug 2025
9 Months active
Languages Used
CSVMATLAB
Technical Skills
Data EngineeringData VisualizationDataset ManagementHyperparameter TuningMachine LearningNeural Networks
PauCliment
PROFILE
Paucliment
Pau Climent developed advanced data generation, optimization, and neural network tooling for the SwanLab/Swan repository, focusing on computational mechanics and scientific computing challenges. Over nine months, he engineered robust MATLAB scripts and CSV-based pipelines to automate dataset creation, model training, and geometric optimization, including Superformula and elliptic tensor workflows. His work emphasized modular code structure, maintainability, and reproducibility, introducing refactored stochastic gradient descent routines, unified cost functions, and enhanced visualization. By integrating deep learning, numerical analysis, and data management, Pau enabled faster experimentation, improved model interpretability, and more reliable benchmarking, demonstrating strong depth in both algorithmic design and software engineering.
Overall Statistics
Feature vs Bugs
94%Features
Repository Contributions
33Total
Bugs
1
Commits
33
Features
16
Lines of code
42,457
Activity Months9
Work History
August 2025
7 Commits • 5 Features
Aug 1, 2025Month: 2025-08 | SwanLab/Swan Monthly Summary: This period delivered key features for geometric optimization, stabilized optimization pipelines, expanded study coverage, and strengthened testing and learning assets. Major bugs fixed and code quality improvements contributed to reliability and maintainability. Business value: enables robust optimization workflows, clearer data management, and richer visualization to inform decision-making.
7 Commits • 5 Features
Aug 1, 2025Month: 2025-08 | SwanLab/Swan Monthly Summary: This period delivered key features for geometric optimization, stabilized optimization pipelines, expanded study coverage, and strengthened testing and learning assets. Major bugs fixed and code quality improvements contributed to reliability and maintainability. Business value: enables robust optimization workflows, clearer data management, and richer visualization to inform decision-making.
August 2025
July 2025
1 Commits • 1 Features
Jul 1, 2025July 2025: Delivered significant feature optimization for the ChomogNetwork tutorial in Swan (SwanLab/Swan), focusing on Superformula parameter calculations and a CSV data refresh to boost performance, accuracy, and visualization fidelity. This work tightens data processing paths, enabling smoother rendering on larger datasets, and establishes groundwork for further optimizations in upcoming sprints. No major bugs reported this month; the focus was on feature optimization and data preparation.
July 2025
1 Commits • 1 Features
Jul 1, 2025July 2025: Delivered significant feature optimization for the ChomogNetwork tutorial in Swan (SwanLab/Swan), focusing on Superformula parameter calculations and a CSV data refresh to boost performance, accuracy, and visualization fidelity. This work tightens data processing paths, enabling smoother rendering on larger datasets, and establishes groundwork for further optimizations in upcoming sprints. No major bugs reported this month; the focus was on feature optimization and data preparation.
June 2025
1 Commits • 1 Features
Jun 1, 2025Concise monthly summary for 2025-06 focusing on key accomplishments, major bugs fixed, impact, and technologies demonstrated. This month centered on delivering the first version of the Superformula dataset generator and database for SwanLab/Swan, expanding the research data generation capabilities and laying groundwork for reproducible experiments.
1 Commits • 1 Features
Jun 1, 2025Concise monthly summary for 2025-06 focusing on key accomplishments, major bugs fixed, impact, and technologies demonstrated. This month centered on delivering the first version of the Superformula dataset generator and database for SwanLab/Swan, expanding the research data generation capabilities and laying groundwork for reproducible experiments.
June 2025
May 2025
7 Commits • 2 Features
May 1, 2025May 2025 SwanLab/Swan: Expanded optimization tutorials and data-generation pipelines, delivering faster, more reliable experimentation and richer design insights. Implemented the Chomog Optimizer Tutorial with Jacobian visualizations; refactored NN Jacobian and backprop for performance; added stiffness/auxetic options and multi-study support to improve precision. Launched the Superformula MicroStruct Problem with MATLAB classes, broader parameter ranges, improved dataset generation, and robust validation. Pipeline improvements include adjusted training epochs, runtime logging, and storage of homogenized tensors/parameters. Overall impact: enhanced capability for design optimization, stronger validation, and clearer business value in client-ready demonstrations.
May 2025
7 Commits • 2 Features
May 1, 2025May 2025 SwanLab/Swan: Expanded optimization tutorials and data-generation pipelines, delivering faster, more reliable experimentation and richer design insights. Implemented the Chomog Optimizer Tutorial with Jacobian visualizations; refactored NN Jacobian and backprop for performance; added stiffness/auxetic options and multi-study support to improve precision. Launched the Superformula MicroStruct Problem with MATLAB classes, broader parameter ranges, improved dataset generation, and robust validation. Pipeline improvements include adjusted training epochs, runtime logging, and storage of homogenized tensors/parameters. Overall impact: enhanced capability for design optimization, stronger validation, and clearer business value in client-ready demonstrations.
March 2025
4 Commits • 2 Features
Mar 1, 2025March 2025 SwanLab/Swan performance summary: Delivered key product features for the SGD training pipeline and neural network gradient tooling, improving training reliability, observability, and maintainability. The work focused on modular cost function architecture, enhanced monitoring via KPI metrics, and added Jacobian/gradient capabilities to support debugging and analysis. Result: faster iteration cycles, clearer cost insights, and stronger analytical capabilities for diagnosing training behavior.
4 Commits • 2 Features
Mar 1, 2025March 2025 SwanLab/Swan performance summary: Delivered key product features for the SGD training pipeline and neural network gradient tooling, improving training reliability, observability, and maintainability. The work focused on modular cost function architecture, enhanced monitoring via KPI metrics, and added Jacobian/gradient capabilities to support debugging and analysis. Result: faster iteration cycles, clearer cost insights, and stronger analytical capabilities for diagnosing training behavior.
March 2025
February 2025
5 Commits • 1 Features
Feb 1, 2025February 2025 monthly summary for SwanLab/Swan focusing on delivering a unified Cost/Training Flow for SGD and a critical bug fix in the FEM Elasticity ellipse generation pipeline. The work improved training modularity, reliability, and dataset generation reproducibility, enabling faster iteration and safer deployment of SGD-based optimization in production.
February 2025
5 Commits • 1 Features
Feb 1, 2025February 2025 monthly summary for SwanLab/Swan focusing on delivering a unified Cost/Training Flow for SGD and a critical bug fix in the FEM Elasticity ellipse generation pipeline. The work improved training modularity, reliability, and dataset generation reproducibility, enabling faster iteration and safer deployment of SGD-based optimization in production.
January 2025
3 Commits • 1 Features
Jan 1, 2025January 2025 performance summary for SwanLab/Swan. Focused on cleaning up the SGD training loop through a targeted refactor to improve readability, maintainability, and extensibility. Delivered a cohesive set of changes that rename costFunction to objectiveFunction, restructure SGD data flow, and separate data handling and loss computation. Also removed SGD-specific data containers, migrating them to a centralized loss function (Sh_Loss_Func), with criteria checks moved to a dedicated function and inline comments to guide future refactors. This groundwork enhances testability, reduces coupling, and enables safer experimentation with the SGD workflow.
3 Commits • 1 Features
Jan 1, 2025January 2025 performance summary for SwanLab/Swan. Focused on cleaning up the SGD training loop through a targeted refactor to improve readability, maintainability, and extensibility. Delivered a cohesive set of changes that rename costFunction to objectiveFunction, restructure SGD data flow, and separate data handling and loss computation. Also removed SGD-specific data containers, migrating them to a centralized loss function (Sh_Loss_Func), with criteria checks moved to a dedicated function and inline comments to guide future refactors. This groundwork enhances testability, reduces coupling, and enables safer experimentation with the SGD workflow.
January 2025
December 2024
1 Commits • 1 Features
Dec 1, 2024December 2024 (SwanLab/Swan): Key milestone achieved with the introduction of a Neural Network Ellipse Model Experimentation Toolkit, enabling systematic exploration of NN architectures on the Chomog_ellipse.csv dataset. Implemented MATLAB scripts including sNN_ellipse_model.m, with tunable regularization (lambda), network size, and layer tapering to support robust performance analysis. This work improves experimentation efficiency, reproducibility, and data-driven model selection, delivering clear business value through faster iteration and better design choices. No major bugs fixed this month. Baseline commit: cbb9031e425fea6da1518663dfa8d3b6f6364c80. Overall impact: enhanced capability to study NN architectures and drive performance improvements in SwanLab/Swan.
December 2024
1 Commits • 1 Features
Dec 1, 2024December 2024 (SwanLab/Swan): Key milestone achieved with the introduction of a Neural Network Ellipse Model Experimentation Toolkit, enabling systematic exploration of NN architectures on the Chomog_ellipse.csv dataset. Implemented MATLAB scripts including sNN_ellipse_model.m, with tunable regularization (lambda), network size, and layer tapering to support robust performance analysis. This work improves experimentation efficiency, reproducibility, and data-driven model selection, delivering clear business value through faster iteration and better design choices. No major bugs fixed this month. Baseline commit: cbb9031e425fea6da1518663dfa8d3b6f6364c80. Overall impact: enhanced capability to study NN architectures and drive performance improvements in SwanLab/Swan.
November 2024
4 Commits • 2 Features
Nov 1, 2024Month: 2024-11 — SwanLab/Swan delivered substantial feature work focused on elliptic tensor modeling and evaluation, enhancing both data/visualization capabilities and model validation workflows. This set of changes strengthens predictive accuracy, interpretability, and the ability to benchmark tensor predictions against ground truth.
4 Commits • 2 Features
Nov 1, 2024Month: 2024-11 — SwanLab/Swan delivered substantial feature work focused on elliptic tensor modeling and evaluation, enhancing both data/visualization capabilities and model validation workflows. This set of changes strengthens predictive accuracy, interpretability, and the ability to benchmark tensor predictions against ground truth.
November 2024
Activity
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Quality Metrics
Correctness83.6%
Maintainability81.8%
Architecture80.6%
Performance71.0%
AI Usage20.6%
Skills & Technologies
Programming Languages
CSVMATLAB
Technical Skills
Algorithm TuningCode CleanupCode RefactoringComputational MechanicsData AnalysisData EngineeringData GenerationData ManagementData OptimizationData PreparationData ProcessingData VisualizationDataset ManagementDeep LearningDocumentation Improvement
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