NVIDIA/physicsnemo
Jul 2025 – Jul 2025
1 Month active
Languages Used
C++PythonYAML
Technical Skills
CFDData EngineeringDeep LearningModel Architecture DesignPhysics-Informed Neural Networks
Sheel N.
PROFILE
Sheel N.
During July 2025, S. Nidhan worked on the NVIDIA/physicsnemo repository, developing flexible global parameter encoding for the DoMINO model. Nidhan refactored the parameter encoding system using Python and C++ to support arbitrary global simulation parameters, including both scalars and vectors, during training and inference. This approach leveraged skills in deep learning, model architecture design, and physics-informed neural networks to enable DoMINO to generalize across diverse simulation conditions with minimal code changes. The work reduced the need for rework when adapting to new experiments, resulting in faster experimentation cycles and more robust, adaptable training and inference pipelines for simulation workflows.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
1Total
Bugs
0
Commits
1
Features
1
Lines of code
510
Activity Months1
Your Network
1376 people
Work History
July 2025
1 Commits • 1 Features
Jul 1, 2025July 2025 — NVIDIA/physicsnemo: Implemented Flexible Global Parameter Encoding for DoMINO, enabling DoMINO to handle arbitrary global simulation parameters during training and inference. Refactored parameter encoding to support a flexible number and types of global parameters (scalars and vectors), improving generalization and adaptability across varying simulation conditions. Commit 6b4cdef25895759f1e7842f5e0c2e5cc497e2a94 implements the changes under (#903). Business impact includes reduced rework when adapting to new experiments, faster experimentation cycles, and more robust training/inference pipelines.
1 Commits • 1 Features
Jul 1, 2025July 2025 — NVIDIA/physicsnemo: Implemented Flexible Global Parameter Encoding for DoMINO, enabling DoMINO to handle arbitrary global simulation parameters during training and inference. Refactored parameter encoding to support a flexible number and types of global parameters (scalars and vectors), improving generalization and adaptability across varying simulation conditions. Commit 6b4cdef25895759f1e7842f5e0c2e5cc497e2a94 implements the changes under (#903). Business impact includes reduced rework when adapting to new experiments, faster experimentation cycles, and more robust training/inference pipelines.
July 2025
Activity
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Quality Metrics
Correctness90.0%
Maintainability80.0%
Architecture90.0%
Performance80.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
C++PythonYAML
Technical Skills
CFDData EngineeringDeep LearningModel Architecture DesignPhysics-Informed Neural Networks
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