flipoyo/MOLONARI1D
Oct 2024 – Nov 2024
2 Months active
Languages Used
Jupyter NotebookPython
Technical Skills
Bayesian InferenceData AnalysisData VisualizationJupyter Notebook UsageMCMCPython Programming
Aurélie Reynaud
PROFILE
Aurélie Reynaud
Worked on the flipoyo/MOLONARI1D repository to develop advanced MCMC DREAM capabilities for geological data analysis, focusing on uncertainty quantification and model reliability. Implemented core MCMC burn-in initialization, DREAM integration, and new sampling methods, supporting both single and multi-chain workflows. Enhanced the analytical workflow with robust visualization of results, parameter tuning for Darcy flow, and improved sensor RMSE display. Leveraged Python, Jupyter Notebooks, and Matplotlib to deliver reproducible, demo-ready Bayesian inference tools. Prioritized code quality through formatting, test cleanup, and removal of outdated files, resulting in a maintainable framework that streamlines scientific computing and data-driven exploration decisions.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
14Total
Bugs
0
Commits
14
Features
6
Lines of code
6,274
Activity Months2
Your Network
65 peopleSame Organization
@etu.minesparis.psl.eu20
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alexandrenoel20Member
Antoine de La RoncièreMember
ArmanDLFMember
barnabetherondMember
Bastien AvrillonMember
Clément GoujonMember
Colin DrouineauMember
Elyes BeloucifMember
Shared Repositories
45
Adam HaïkMember
alexandrenoel20Member
Antoine de La RoncièreMember
ArmanDLFMember
barnabetherondMember
Barnabé ThérondMember
Bastien AvrillonMember
Ombline BrunetMember
Clément GoujonMember
Work History
November 2024
12 Commits • 4 Features
Nov 1, 2024November 2024: flipoyo/MOLONARI1D – Delivered core MCMC burn-in initialization and DREAM integration, added DREAM sampling methods, refreshed demo notebook, and performed comprehensive visualization, parameter tuning, and code quality improvements. These changes establish a robust, demo-ready framework for Bayesian inference with MCMC/DREAM and improve maintainability.
12 Commits • 4 Features
Nov 1, 2024November 2024: flipoyo/MOLONARI1D – Delivered core MCMC burn-in initialization and DREAM integration, added DREAM sampling methods, refreshed demo notebook, and performed comprehensive visualization, parameter tuning, and code quality improvements. These changes establish a robust, demo-ready framework for Bayesian inference with MCMC/DREAM and improve maintainability.
November 2024
October 2024
2 Commits • 2 Features
Oct 1, 2024October 2024 — flipoyo/MOLONARI1D: Implemented new advanced MCMC DREAM capabilities with visualization and stability improvements for heat-map generation. These deliverables enhance geological data analysis, uncertainty quantification, and model reliability, directly supporting data-driven exploration decisions and faster validation cycles.
October 2024
2 Commits • 2 Features
Oct 1, 2024October 2024 — flipoyo/MOLONARI1D: Implemented new advanced MCMC DREAM capabilities with visualization and stability improvements for heat-map generation. These deliverables enhance geological data analysis, uncertainty quantification, and model reliability, directly supporting data-driven exploration decisions and faster validation cycles.
Activity
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Quality Metrics
Correctness80.6%
Maintainability81.4%
Architecture79.4%
Performance68.0%
AI Usage23.0%
Skills & Technologies
Programming Languages
Jupyter NotebookPython
Technical Skills
Algorithm implementationBayesian InferenceBayesian inferenceCode FormattingData AnalysisData CleaningData VisualizationData analysisJupyter Notebook UsageJupyter NotebooksMCMCMCMC SimulationMatplotlibNumerical SimulationNumerical methods
Repositories Contributed To
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