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
Contributed to the idaholab/moose repository by developing a C++ LibtorchModel class that enables evaluation of pre-trained neural networks within the NEML2 framework. The implementation supports arbitrary input and output mappings, as well as linear scaling, and includes comprehensive tests and documentation. Demonstrated the feature’s integration through a heat conduction example, establishing a foundation for AI-enabled modeling in the codebase. Additionally, improved the LibTorch installation documentation by correcting the test script invocation, reducing user errors and streamlining onboarding. Work focused on C++, machine learning, and documentation, delivering targeted enhancements that addressed both core functionality and user experience.
Overall Statistics
Feature vs Bugs
50%Features
Repository Contributions
3Total
Bugs
1
Commits
3
Features
1
Lines of code
416
Activity Months2
Your Network
210 peopleSame Organization
@inl.gov60
Shared Repositories
150
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Ansh ChaubeMember
Alex GrantMember
Alexander BlairMember
Alex LindsayMember
Alex LindsayMember
Andreas Henrik FrederiksenMember
WilkAndyMember
Aaron OaksMember
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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