pmgbergen/porepy
Apr 2026 – Jun 2026
2 Months active
Languages Used
Python
Technical Skills
Python scriptingnumerical modelingscientific computingPythonScientific Computing
aband
PROFILE
Aband
Contributed to the pmgbergen/porepy repository by developing executable entry points for the Mandel and Terzaghi Biot models, enabling direct execution for validation and regression testing. Leveraging Python and scientific computing skills, introduced main functions that streamlined end-to-end model verification and improved traceability. Later, enhanced the benchmark suite to support dynamic flow regimes by parameterizing execution utilities and removing hardcoded defaults, which automated and standardized benchmark runs across multiple 2D and 3D cases. Applied test-driven development and numerical modeling techniques to expand test coverage, improve reproducibility, and reduce manual setup, thereby strengthening the repository’s automation and continuous integration readiness.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
3Total
Bugs
0
Commits
3
Features
2
Lines of code
182
Activity Months2
Work History
June 2026
2 Commits • 1 Features
Jun 1, 2026In June 2026, the porepy benchmark suite was upgraded to support dynamic flow regimes, delivering automated, reproducible benchmark runs across multiple 2D/3D cases. Key changes include the Run Example Utilities for Flow Benchmarks across several benchmarks and the extension of run_example in fracture_damage.py to accept a regimes parameter, removing hardcoded defaults. This enables streamlined execution and automatic model retrieval for flow simulations, significantly improving experimentation throughput and consistency. Major improvements fixed issues related to hardcoded defaults by parameterizing regimes and adding tests to validate new utilities. Commit work focused on test-driven development (TST) across diverse benchmarks: flow_benchmark_2d case3 and 4, flow_benchmark_3d_case3, mandel_biot, terzaghi_biot, and tracer_flow; and the fracture_damage.py run_example regime parameterization. Overall impact includes improved automation, reproducibility, and scalability of benchmark runs, reducing manual setup and time-to-insight for model validation and performance tuning. These changes strengthen CI readiness and onboarding for new benchmarks. Technologies and skills demonstrated include Python, benchmark tooling, test-driven development, and parameterization patterns for dynamic configurations.
2 Commits • 1 Features
Jun 1, 2026In June 2026, the porepy benchmark suite was upgraded to support dynamic flow regimes, delivering automated, reproducible benchmark runs across multiple 2D/3D cases. Key changes include the Run Example Utilities for Flow Benchmarks across several benchmarks and the extension of run_example in fracture_damage.py to accept a regimes parameter, removing hardcoded defaults. This enables streamlined execution and automatic model retrieval for flow simulations, significantly improving experimentation throughput and consistency. Major improvements fixed issues related to hardcoded defaults by parameterizing regimes and adding tests to validate new utilities. Commit work focused on test-driven development (TST) across diverse benchmarks: flow_benchmark_2d case3 and 4, flow_benchmark_3d_case3, mandel_biot, terzaghi_biot, and tracer_flow; and the fracture_damage.py run_example regime parameterization. Overall impact includes improved automation, reproducibility, and scalability of benchmark runs, reducing manual setup and time-to-insight for model validation and performance tuning. These changes strengthen CI readiness and onboarding for new benchmarks. Technologies and skills demonstrated include Python, benchmark tooling, test-driven development, and parameterization patterns for dynamic configurations.
June 2026
April 2026
1 Commits • 1 Features
Apr 1, 2026April 2026: Implemented executable entry points for Mandel and Terzaghi Biot models in the porepy repository, enabling direct execution for validation and testing. This enhancement improves the QA workflow by providing reproducible, end-to-end model runs and simplifies regression testing. Work is traceable to a dedicated commit with clear attribution.
April 2026
1 Commits • 1 Features
Apr 1, 2026April 2026: Implemented executable entry points for Mandel and Terzaghi Biot models in the porepy repository, enabling direct execution for validation and testing. This enhancement improves the QA workflow by providing reproducible, end-to-end model runs and simplifies regression testing. Work is traceable to a dedicated commit with clear attribution.
Activity
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Quality Metrics
Correctness86.6%
Maintainability86.6%
Architecture80.0%
Performance86.6%
AI Usage53.4%
Skills & Technologies
Programming Languages
Python
Technical Skills
PythonPython scriptingScientific Computingnumerical modelingscientific computing
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