idaholab/moose
Apr 2024 – May 2025
7 Months active
Languages Used
C++PythonShellYAMLJSON
Technical Skills
Active LearningBayesian InferenceBayesian inferenceC++C++ developmentGaussian processes
somu15
PROFILE
Somu15
Contributed to the idaholab/moose repository by developing advanced machine learning and Bayesian inference workflows, focusing on scalable uncertainty quantification and active learning for scientific modeling. Leveraged C++ and Python to integrate Gaussian processes, neural networks, and MCMC sampling, enabling dynamic retraining, parallel acquisition, and robust model calibration. Automated installation and environment setup using shell scripting, improving reproducibility and onboarding. Enhanced stochastic modeling tools with reliability fixes and modular design, supporting efficient experimentation and Bayesian optimization. Maintained code quality through targeted refactoring, cleanup, and test coverage, resulting in a maintainable, high-performance framework for predictive modeling and uncertainty analysis in research applications.
Overall Statistics
Feature vs Bugs
93%Features
Repository Contributions
25Total
Bugs
1
Commits
25
Features
13
Lines of code
561,426
Activity Months7
Your Network
200 peopleSame Organization
@inl.gov57
Bradley HuddlestonMember
BrettMember
Derek VranesMember
Butterworth, EvanMember
Gabriel J. SotoMember
Jason M MillerMember
Joseph CoronaVazquezMember
Kevin HarwoodMember
Larry AagesenMember
Shared Repositories
143
Riet, Adriaan AnthonyMember
Ansh ChaubeMember
Alex GrantMember
Alexander BlairMember
Alex LindsayMember
Alex LindsayMember
Andreas Henrik FrederiksenMember
WilkAndyMember
Aaron OaksMember
Work History
May 2025
1 Commits
May 1, 2025May 2025 monthly summary for idaholab/moose: Focused on reliability improvements for stochastic tools used in active learning samplers and Gaussian process models. Implemented critical bug fixes and configuration hardening to stabilize ML experimentation pipelines, improving end-to-end reliability and reducing failed runs. This work enhances business value by enabling faster, more reliable experimentation and robust model tooling within the Moose framework.
1 Commits
May 1, 2025May 2025 monthly summary for idaholab/moose: Focused on reliability improvements for stochastic tools used in active learning samplers and Gaussian process models. Implemented critical bug fixes and configuration hardening to stabilize ML experimentation pipelines, improving end-to-end reliability and reducing failed runs. This work enhances business value by enabling faster, more reliable experimentation and robust model tooling within the Moose framework.
May 2025
February 2025
1 Commits • 1 Features
Feb 1, 2025February 2025 (2025-02) — Installation workflow modernization for idaholab/moose completed by removing the obsolete Miniforge3-Linux-x86_64.sh installer and aligning the setup with updated dependencies. The change reduces setup friction, improves environment reproducibility across platforms, and lowers ongoing maintenance. The work is captured in commit 8cee32a87299a32a5b0d38f9b855c14c157080bc (message: tests2 #28196). No major bugs were reported this month; focus was on stabilizing tooling and installer paths to support faster onboarding and future updates.
February 2025
1 Commits • 1 Features
Feb 1, 2025February 2025 (2025-02) — Installation workflow modernization for idaholab/moose completed by removing the obsolete Miniforge3-Linux-x86_64.sh installer and aligning the setup with updated dependencies. The change reduces setup friction, improves environment reproducibility across platforms, and lowers ongoing maintenance. The work is captured in commit 8cee32a87299a32a5b0d38f9b855c14c157080bc (message: tests2 #28196). No major bugs were reported this month; focus was on stabilizing tooling and installer paths to support faster onboarding and future updates.
November 2024
2 Commits • 1 Features
Nov 1, 2024Month: 2024-11 — Delivered major Bayesian framework enhancements for idaholab/moose, focusing on refactoring stochastic tools and MCMC decision logic, introducing LinearSum to improve model-data fit evaluation, and performing code cleanup by removing obsolete files. Implemented a parallel acquisition system for Bayesian active learning to accelerate model training through parallel evaluation of input conditions, along with several acquisition functions and parallel-processing support in related classes. Maintained strong code hygiene with targeted commits including rebasing/cleanup and initial tests. No user-facing bug fixes this month; emphasis was on reliability, performance, and maintainability.
2 Commits • 1 Features
Nov 1, 2024Month: 2024-11 — Delivered major Bayesian framework enhancements for idaholab/moose, focusing on refactoring stochastic tools and MCMC decision logic, introducing LinearSum to improve model-data fit evaluation, and performing code cleanup by removing obsolete files. Implemented a parallel acquisition system for Bayesian active learning to accelerate model training through parallel evaluation of input conditions, along with several acquisition functions and parallel-processing support in related classes. Maintained strong code hygiene with targeted commits including rebasing/cleanup and initial tests. No user-facing bug fixes this month; emphasis was on reliability, performance, and maintainability.
November 2024
July 2024
8 Commits • 3 Features
Jul 1, 2024July 2024 monthly summary for idaholab/moose: Delivered scalable Bayesian inference enhancements and Bayesian optimization capabilities, driving faster, more accurate models and broader research use cases. Key features delivered include: 1) Active Learning enhancements for Bayesian inference with Gaussian processes: generic AL classes, Genericsampler for parallel AL, active learning reporters, trainer mods, GP evaluation, and cleanup of unused objects. 2) Bayesian optimization acquisition functions: introduced multiple acquisition functions to broaden optimization strategies. 3) GP-based PMCMC decision-making for MCMC sampling: generalized PMCMCDecision and GP-based decision-making to improve sampling efficiency and accuracy. Major maintenance/cleanup: removed stray objects created during AL experiments to improve stability. Impact: improved sample efficiency and predictive performance, enabled richer Bayesian workflows, and increased maintainability; Technologies demonstrated: modular design, Gaussian process integration, parallel AL, acquisition functions, GP-based PMCMC, and monitoring reporters.
July 2024
8 Commits • 3 Features
Jul 1, 2024July 2024 monthly summary for idaholab/moose: Delivered scalable Bayesian inference enhancements and Bayesian optimization capabilities, driving faster, more accurate models and broader research use cases. Key features delivered include: 1) Active Learning enhancements for Bayesian inference with Gaussian processes: generic AL classes, Genericsampler for parallel AL, active learning reporters, trainer mods, GP evaluation, and cleanup of unused objects. 2) Bayesian optimization acquisition functions: introduced multiple acquisition functions to broaden optimization strategies. 3) GP-based PMCMC decision-making for MCMC sampling: generalized PMCMCDecision and GP-based decision-making to improve sampling efficiency and accuracy. Major maintenance/cleanup: removed stray objects created during AL experiments to improve stability. Impact: improved sample efficiency and predictive performance, enabled richer Bayesian workflows, and increased maintainability; Technologies demonstrated: modular design, Gaussian process integration, parallel AL, acquisition functions, GP-based PMCMC, and monitoring reporters.
June 2024
2 Commits • 2 Features
Jun 1, 2024June 2024 monthly summary for idaholab/moose: Delivered features focused on deployment automation and MCMC sampling enhancements. Key deliverables include Miniforge3 Installer Script and MCMC improvements with AIDESGPryTest/AIDESGPryTestTransform, with detailed commit references. No major bugs fixed in this period. Impact includes reduced onboarding/setup time and improved parallel sampling reliability, enabling more scalable analyses. Technologies demonstrated include shell scripting, Python class design for MCMC, and Gaussian process integration.
2 Commits • 2 Features
Jun 1, 2024June 2024 monthly summary for idaholab/moose: Delivered features focused on deployment automation and MCMC sampling enhancements. Key deliverables include Miniforge3 Installer Script and MCMC improvements with AIDESGPryTest/AIDESGPryTestTransform, with detailed commit references. No major bugs fixed in this period. Impact includes reduced onboarding/setup time and improved parallel sampling reliability, enabling more scalable analyses. Technologies demonstrated include shell scripting, Python class design for MCMC, and Gaussian process integration.
June 2024
May 2024
5 Commits • 3 Features
May 1, 2024In May 2024, idaholab/moose delivered substantial progress in Bayesian inference, stochastic modeling, and Active Learning simplification, with a clear focus on reliability, scalability, and business value. The changes span advanced inference capabilities, enhanced stochastic dynamics for ecological modeling, and efficiency-driven refactoring of active learning components.
May 2024
5 Commits • 3 Features
May 1, 2024In May 2024, idaholab/moose delivered substantial progress in Bayesian inference, stochastic modeling, and Active Learning simplification, with a clear focus on reliability, scalability, and business value. The changes span advanced inference capabilities, enhanced stochastic dynamics for ecological modeling, and efficiency-driven refactoring of active learning components.
April 2024
6 Commits • 3 Features
Apr 1, 2024April 2024 monthly summary for idaholab/moose. This period focused on delivering ML-enabled capabilities for improved modeling workflows and scalable uncertainty quantification, with no explicit major bugs fixed this month. Key features delivered: - Neural Network Binary Classification Support: Enable neural networks to operate in binary classification mode by switching between regression and classification modes, and adopt cross-entropy loss for binary tasks. - Active Learning Neural Network with LibTorch: Implement an active learning workflow for neural networks using LibTorch, enabling dynamic retraining with new data, standardizing inputs/outputs, and loading existing models for further training. - GPry-based Bayesian Inference Integration with NN/GP in MOOSE: Integrate GPry-based Bayesian sampling into the MOOSE framework, adding BayesianGPryLearner and extending components to support NN/GP integration for fast Bayesian inference and PMCMC-based sampling. Major bugs fixed: - No explicit bug fixes logged in this period; focused on feature delivery and establishing ML-enabled foundations. Overall impact and accomplishments: - Established a robust ML-enabled workflow within MOOS ESE to support rapid experimentation, retraining with fresh data, and uncertainty quantification. - Enabled faster iteration cycles for predictive modeling through active learning and model reuse. - Laid groundwork for advanced Bayesian inference and PMCMC-based sampling, improving model calibration and decision support. Technologies/skills demonstrated: - LibTorch integration for PyTorch-like workflows in a C++/MOOSE environment. - Active learning pipelines, data standardization, and model persistence. - GPry-based Bayesian sampling, NN/GP integration, and PMCMC foundations for scalable inference.
6 Commits • 3 Features
Apr 1, 2024April 2024 monthly summary for idaholab/moose. This period focused on delivering ML-enabled capabilities for improved modeling workflows and scalable uncertainty quantification, with no explicit major bugs fixed this month. Key features delivered: - Neural Network Binary Classification Support: Enable neural networks to operate in binary classification mode by switching between regression and classification modes, and adopt cross-entropy loss for binary tasks. - Active Learning Neural Network with LibTorch: Implement an active learning workflow for neural networks using LibTorch, enabling dynamic retraining with new data, standardizing inputs/outputs, and loading existing models for further training. - GPry-based Bayesian Inference Integration with NN/GP in MOOSE: Integrate GPry-based Bayesian sampling into the MOOSE framework, adding BayesianGPryLearner and extending components to support NN/GP integration for fast Bayesian inference and PMCMC-based sampling. Major bugs fixed: - No explicit bug fixes logged in this period; focused on feature delivery and establishing ML-enabled foundations. Overall impact and accomplishments: - Established a robust ML-enabled workflow within MOOS ESE to support rapid experimentation, retraining with fresh data, and uncertainty quantification. - Enabled faster iteration cycles for predictive modeling through active learning and model reuse. - Laid groundwork for advanced Bayesian inference and PMCMC-based sampling, improving model calibration and decision support. Technologies/skills demonstrated: - LibTorch integration for PyTorch-like workflows in a C++/MOOSE environment. - Active learning pipelines, data standardization, and model persistence. - GPry-based Bayesian sampling, NN/GP integration, and PMCMC foundations for scalable inference.
April 2024
Activity
Loading activity data...
Quality Metrics
Correctness84.8%
Maintainability84.0%
Architecture84.8%
Performance82.4%
AI Usage33.6%
Skills & Technologies
Programming Languages
C++JSONPythonShellYAML
Technical Skills
Active LearningAlgorithm DesignBayesian InferenceBayesian inferenceBayesian optimizationC++C++ DevelopmentC++ developmentC++ programmingContinuous IntegrationDevOpsGaussian ProcessesGaussian processesInstallation automationLibTorch
Repositories Contributed To
Overview of all repositories you've contributed to across your timeline