idaholab/moose
Apr 2025 – May 2025
2 Months active
Languages Used
MarkdownC++INIShell
Technical Skills
DocumentationC++Machine LearningModel EvaluationNeural NetworksTesting
ChaitanyaBhave-e
PROFILE
Chaitanyabhave-e
Chaitanya Bhave developed and integrated a C++ LibtorchModel class within the idaholab/moose repository, enabling evaluation of pre-trained neural networks in the NEML2 module. The implementation supported arbitrary input and output mappings, as well as linear scaling, and included comprehensive tests and documentation. Chaitanya demonstrated the feature’s application through a heat conduction example, establishing a foundation for AI-enabled modeling workflows. Additionally, he improved the LibTorch installation documentation by correcting test script instructions, reducing onboarding errors for new users. His work combined C++, machine learning, and documentation skills, delivering both a robust feature and enhanced user guidance within two months.
Overall Statistics
Feature vs Bugs
50%Features
Repository Contributions
3Total
Bugs
1
Commits
3
Features
1
Lines of code
416
Activity Months2
Your Network
181 peopleSame Organization
@inl.gov52
Shared Repositories
129
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WilkAndyMember
Aaron OaksMember
Ben SpencerMember
moosetestMember
Work History
May 2025
2 Commits • 1 Features
May 1, 2025May 2025 Monthly Summary: Implemented LibtorchModel integration to evaluate pre-trained neural networks within NEML2 for idaholab/moose. Delivered a C++ LibtorchModel class, accompanying tests and documentation, and demonstrated end-to-end usage in a heat conduction scenario. The feature supports arbitrary input/output mappings and linear input/output scaling, establishing a core AI-enabled modeling capability within the framework.
2 Commits • 1 Features
May 1, 2025May 2025 Monthly Summary: Implemented LibtorchModel integration to evaluate pre-trained neural networks within NEML2 for idaholab/moose. Delivered a C++ LibtorchModel class, accompanying tests and documentation, and demonstrated end-to-end usage in a heat conduction scenario. The feature supports arbitrary input/output mappings and linear input/output scaling, establishing a core AI-enabled modeling capability within the framework.
May 2025
April 2025
1 Commits
Apr 1, 2025April 2025 monthly summary for idaholab/moose: Focused improvement on LibTorch installation documentation to reduce user errors and improve onboarding. The primary change corrected the test invocation in the LibTorch installation guide, ensuring users run the correct script and complete tests as intended. This aligns with existing documentation standards and supports smoother LibTorch adoption within MOOSE workflows.
April 2025
1 Commits
Apr 1, 2025April 2025 monthly summary for idaholab/moose: Focused improvement on LibTorch installation documentation to reduce user errors and improve onboarding. The primary change corrected the test invocation in the LibTorch installation guide, ensuring users run the correct script and complete tests as intended. This aligns with existing documentation standards and supports smoother LibTorch adoption within MOOSE workflows.
Activity
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Quality Metrics
Correctness90.0%
Maintainability90.0%
Architecture90.0%
Performance86.6%
AI Usage33.4%
Skills & Technologies
Programming Languages
C++INIMarkdownShell
Technical Skills
C++DocumentationMachine LearningModel EvaluationNeural NetworksTesting
Repositories Contributed To
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