NVIDIA/physicsnemo
Nov 2025 – Apr 2026
2 Months active
Languages Used
Python
Technical Skills
PyTorchdata processingmachine learningmodel validationattention mechanismsdeep learning
Deepak Akhare
PROFILE
Deepak Akhare
Contributed to NVIDIA/physicsnemo by developing two core features over two months, focusing on deep learning workflows and attention mechanisms. Built a configurable model training validation and checkpointing system in Python using PyTorch, enabling periodic evaluation on separate datasets and improving reproducibility and traceability in model development. Later, implemented GALE_FA, a geometry-aware attention mechanism for the GeoTransolver module, introducing cross-attention and enhanced latent embeddings for physics-based simulations. This work involved backend refactoring, expanded test coverage, and compatibility updates. Emphasized maintainable code and robust validation, leveraging skills in neural networks, data processing, and machine learning to strengthen the repository’s capabilities.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
2Total
Bugs
0
Commits
2
Features
2
Lines of code
1,382
Activity Months2
Your Network
1710 peopleSame Organization
@nvidia.com1667
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43
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Work History
April 2026
1 Commits • 1 Features
Apr 1, 2026April 2026 monthly summary for NVIDIA/physicsnemo: Delivered GALE_FA, a geometry-aware attention mechanism for GeoTransolver, enabling cross-attention and improved geometry-aware latent embeddings. The work included backend refactoring, test updates, and compatibility hardening to ensure a smooth transition from GAFLARE to GALE_FA and support for future extensions in physics-based simulations.
1 Commits • 1 Features
Apr 1, 2026April 2026 monthly summary for NVIDIA/physicsnemo: Delivered GALE_FA, a geometry-aware attention mechanism for GeoTransolver, enabling cross-attention and improved geometry-aware latent embeddings. The work included backend refactoring, test updates, and compatibility hardening to ensure a smooth transition from GAFLARE to GALE_FA and support for future extensions in physics-based simulations.
April 2026
November 2025
1 Commits • 1 Features
Nov 1, 2025November 2025 monthly summary for NVIDIA/physicsnemo focused on strengthening training workflows through validation and checkpointing, with an emphasis on reproducibility, evaluation quality, and traceability.
November 2025
1 Commits • 1 Features
Nov 1, 2025November 2025 monthly summary for NVIDIA/physicsnemo focused on strengthening training workflows through validation and checkpointing, with an emphasis on reproducibility, evaluation quality, and traceability.
Activity
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Quality Metrics
Correctness80.0%
Maintainability80.0%
Architecture80.0%
Performance80.0%
AI Usage40.0%
Skills & Technologies
Programming Languages
Python
Technical Skills
PyTorchattention mechanismsdata processingdeep learningmachine learningmodel validationneural networks
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