NVIDIA/physicsnemo
Nov 2024 – Feb 2026
12 Months active
Languages Used
BashC++PythonMarkdownYAMLShell
Technical Skills
AWS S3Computational Fluid Dynamics (CFD)DGLData PreprocessingDeep LearningDistributed Training
Mohammad Amin Nabian
PROFILE
Mohammad Amin Nabian
M.A. Nabiyan developed advanced machine learning and simulation features for the NVIDIA/physicsnemo repository, focusing on neural network architectures for aerodynamics and structural mechanics. He engineered scalable pipelines using Python and PyTorch, integrating graph neural networks and deep learning for tasks such as aerodynamic evaluation and deforming plate simulations. His work included robust data preprocessing, distributed training, and reproducible workflows, with careful attention to code organization, documentation, and dependency management. Nabiyan also addressed reliability through targeted bug fixes and input validation, enhancing onboarding and maintainability. His contributions demonstrated depth in scientific computing, model deployment, and cross-domain workflow integration within complex ML systems.
Overall Statistics
Feature vs Bugs
85%Features
Repository Contributions
24Total
Bugs
3
Commits
24
Features
17
Lines of code
16,369
Activity Months12
Your Network
33 people
Work History
February 2026
4 Commits • 3 Features
Feb 1, 2026February 2026 monthly summary for NVIDIA/physicsnemo: Key features delivered, major bugs fixed, and overall impact for the business and engineering goals. Focused on reliability, clarity, and maintainability of examples and external models.
4 Commits • 3 Features
Feb 1, 2026February 2026 monthly summary for NVIDIA/physicsnemo: Key features delivered, major bugs fixed, and overall impact for the business and engineering goals. Focused on reliability, clarity, and maintainability of examples and external models.
February 2026
January 2026
2 Commits • 1 Features
Jan 1, 2026January 2026 monthly summary for NVIDIA/physicsnemo focusing on architectural refactor and robustness improvements within the MeshGraphNet family. Delivered a major refactor of the MeshGraphNet architecture (including MeshGraphNet, BiStrideMeshGraphNet, and HybridMeshGraphNet) with enhanced parameter documentation and robust input validation for shapes and dimensions. These changes improve clarity, reduce configuration errors, and lay a solid foundation for future features and performance work. No explicit bug fixes were documented this month; the work emphasizes maintainability, correctness, and safer defaults. Notable commits include f393908ec3e6bf8915c2d939f0365ee182f28bb0 (MGN Refactor #1324) and 7806dbe31cb8c3efd4785690468826d7aaae4c54 (Refactor MGN variants #1328).
January 2026
2 Commits • 1 Features
Jan 1, 2026January 2026 monthly summary for NVIDIA/physicsnemo focusing on architectural refactor and robustness improvements within the MeshGraphNet family. Delivered a major refactor of the MeshGraphNet architecture (including MeshGraphNet, BiStrideMeshGraphNet, and HybridMeshGraphNet) with enhanced parameter documentation and robust input validation for shapes and dimensions. These changes improve clarity, reduce configuration errors, and lay a solid foundation for future features and performance work. No explicit bug fixes were documented this month; the work emphasizes maintainability, correctness, and safer defaults. Notable commits include f393908ec3e6bf8915c2d939f0365ee182f28bb0 (MGN Refactor #1324) and 7806dbe31cb8c3efd4785690468826d7aaae4c54 (Refactor MGN variants #1328).
November 2025
2 Commits • 1 Features
Nov 1, 2025November 2025 — NVIDIA/physicsnemo focused on quality improvements for the crash sample module through documentation and type-checking enhancements. Key changes included clarifying the crash sample README, removing outdated roof crash modeling content, and adding jaxtyping as a dependency to improve type checking, reliability, and maintainability. These efforts reduce onboarding time for new contributors, minimize misconfigurations, and lay groundwork for safer future changes. Commit-level changes delivered this month include updates to the crash readme and addition of jaxtyping to the requirements, enabling stronger static analysis and more robust maintenance. Overall impact: clearer documentation, improved code quality, and stronger typing support, driving faster, safer iterations and reduced maintenance costs.
2 Commits • 1 Features
Nov 1, 2025November 2025 — NVIDIA/physicsnemo focused on quality improvements for the crash sample module through documentation and type-checking enhancements. Key changes included clarifying the crash sample README, removing outdated roof crash modeling content, and adding jaxtyping as a dependency to improve type checking, reliability, and maintainability. These efforts reduce onboarding time for new contributors, minimize misconfigurations, and lay groundwork for safer future changes. Commit-level changes delivered this month include updates to the crash readme and addition of jaxtyping to the requirements, enabling stronger static analysis and more robust maintenance. Overall impact: clearer documentation, improved code quality, and stronger typing support, driving faster, safer iterations and reduced maintenance costs.
November 2025
October 2025
1 Commits
Oct 1, 2025October 2025 monthly summary for NVIDIA/physicsnemo: Delivered a reliability-focused fix to robust STL file processing by skipping already processed files and existing outputs, ensuring idempotent runs and preventing duplicate solids. The change prevents redundant processing and potential errors, improving automation stability and data integrity for downstream simulations. The fix was implemented via commit 99781d0dbd45b8c8ba2bcb66e6311813ae55e46a and accompanied by an update to CHANGELOG.md. Impact includes reduced rework, smoother CI/CD pipelines, and stronger release hygiene. Technologies and skills demonstrated: Python scripting, file I/O validation, code hygiene, and clear release notes.
October 2025
1 Commits
Oct 1, 2025October 2025 monthly summary for NVIDIA/physicsnemo: Delivered a reliability-focused fix to robust STL file processing by skipping already processed files and existing outputs, ensuring idempotent runs and preventing duplicate solids. The change prevents redundant processing and potential errors, improving automation stability and data integrity for downstream simulations. The fix was implemented via commit 99781d0dbd45b8c8ba2bcb66e6311813ae55e46a and accompanied by an update to CHANGELOG.md. Impact includes reduced rework, smoother CI/CD pipelines, and stronger release hygiene. Technologies and skills demonstrated: Python scripting, file I/O validation, code hygiene, and clear release notes.
September 2025
1 Commits
Sep 1, 2025September 2025: Stabilized datacenter data loading by aligning MeshDatapipe.parallel with the data pipeline, resolving a RuntimeError in the datacenter example. The fix, implemented in commit 096a66fb1d4177cdf60f8cea9cff303671fe78e5 (#1134), makes dali.fn.external_source parallelism controllable via MeshDatapipe.parallel, defaulting to True and disabled in training/validation to ensure deterministic loading. Impact: fewer interruptions in large-scale data ingestion, smoother training runs, and improved reliability.
1 Commits
Sep 1, 2025September 2025: Stabilized datacenter data loading by aligning MeshDatapipe.parallel with the data pipeline, resolving a RuntimeError in the datacenter example. The fix, implemented in commit 096a66fb1d4177cdf60f8cea9cff303671fe78e5 (#1134), makes dali.fn.external_source parallelism controllable via MeshDatapipe.parallel, defaulting to True and disabled in training/validation to ensure deterministic loading. Impact: fewer interruptions in large-scale data ingestion, smoother training runs, and improved reliability.
September 2025
August 2025
2 Commits • 2 Features
Aug 1, 2025For 2025-08, delivered two major features in NVIDIA/physicsnemo that expand modeling fidelity and deployment readiness. Implemented Hybrid MeshGraphNet for the deforming plate, introducing separate processing of mesh and world edges, with comprehensive updates to data loading, model components, training scripts, and inference to enable advanced deformation modeling. Added inference capabilities for the x-meshgraphnet example focusing on external aerodynamics, including a new inference script, configuration updates, and user-facing documentation to run predictions and evaluate against ground truth. No major bugs fixed this month; focus was on feature delivery, code quality, and documentation. These workstreams improve predictive accuracy for structural deformations and aerodynamic simulations, streamline evaluation workflows, and demonstrate proficiency with graph-based neural networks and ML deployment tooling.
August 2025
2 Commits • 2 Features
Aug 1, 2025For 2025-08, delivered two major features in NVIDIA/physicsnemo that expand modeling fidelity and deployment readiness. Implemented Hybrid MeshGraphNet for the deforming plate, introducing separate processing of mesh and world edges, with comprehensive updates to data loading, model components, training scripts, and inference to enable advanced deformation modeling. Added inference capabilities for the x-meshgraphnet example focusing on external aerodynamics, including a new inference script, configuration updates, and user-facing documentation to run predictions and evaluate against ground truth. No major bugs fixed this month; focus was on feature delivery, code quality, and documentation. These workstreams improve predictive accuracy for structural deformations and aerodynamic simulations, streamline evaluation workflows, and demonstrate proficiency with graph-based neural networks and ML deployment tooling.
July 2025
2 Commits • 2 Features
Jul 1, 2025July 2025 NVIDIA/physicsnemo monthly summary focusing on delivering high-value features for usability and advanced modeling, with emphasis on business impact and technical excellence.
2 Commits • 2 Features
Jul 1, 2025July 2025 NVIDIA/physicsnemo monthly summary focusing on delivering high-value features for usability and advanced modeling, with emphasis on business impact and technical excellence.
July 2025
June 2025
4 Commits • 2 Features
Jun 1, 2025June 2025 monthly summary for NVIDIA/physicsnemo: Focused on improving discoverability and reproducibility of example workloads. Key features delivered: reorganized example directories and updated docs, and added per-example dependency tooling. Major bugs fixed: none identified in this period. Overall impact: reduced onboarding time, faster experiment setup, and better maintainability across the repository. Technologies/skills demonstrated: repository organization, Python packaging (requirements.txt), documentation updates, and cross-domain knowledge across weather and CFD domains.
June 2025
4 Commits • 2 Features
Jun 1, 2025June 2025 monthly summary for NVIDIA/physicsnemo: Focused on improving discoverability and reproducibility of example workloads. Key features delivered: reorganized example directories and updated docs, and added per-example dependency tooling. Major bugs fixed: none identified in this period. Overall impact: reduced onboarding time, faster experiment setup, and better maintainability across the repository. Technologies/skills demonstrated: repository organization, Python packaging (requirements.txt), documentation updates, and cross-domain knowledge across weather and CFD domains.
May 2025
1 Commits • 1 Features
May 1, 2025May 2025: Delivered an end-to-end MeshGraphNet-based structural mechanics example for a deforming plate in NVIDIA/physicsnemo, including training, inference, data loading, documentation, and a dataset download script. This feature (commit a6c256e2c51b9abc4c7d7081febc936cb7934d80) enhances reproducibility, accelerates onboarding, and broadens the repo's applicability to deformable-body simulations. No major bugs reported this month; the focus was on delivering a robust, user-friendly workflow for researchers and engineers.
1 Commits • 1 Features
May 1, 2025May 2025: Delivered an end-to-end MeshGraphNet-based structural mechanics example for a deforming plate in NVIDIA/physicsnemo, including training, inference, data loading, documentation, and a dataset download script. This feature (commit a6c256e2c51b9abc4c7d7081febc936cb7934d80) enhances reproducibility, accelerates onboarding, and broadens the repo's applicability to deformable-body simulations. No major bugs reported this month; the focus was on delivering a robust, user-friendly workflow for researchers and engineers.
May 2025
February 2025
1 Commits • 1 Features
Feb 1, 2025February 2025: Focused on establishing technical documentation scaffolding to support HydroGraphNet development within the NVIDIA/physicsnemo flood modeling example. Delivered a placeholder README to mark future model integration and align with roadmap; no major bug fixes this month. This work improves onboarding, planning, and future maintainability; demonstrates proficiency with Git-based documentation, Markdown, and repository hygiene.
February 2025
1 Commits • 1 Features
Feb 1, 2025February 2025: Focused on establishing technical documentation scaffolding to support HydroGraphNet development within the NVIDIA/physicsnemo flood modeling example. Delivered a placeholder README to mark future model integration and align with roadmap; no major bug fixes this month. This work improves onboarding, planning, and future maintainability; demonstrates proficiency with Git-based documentation, Markdown, and repository hygiene.
January 2025
3 Commits • 3 Features
Jan 1, 2025January 2025 (Month: 2025-01) — NVIDIA/physicsnemo. Focused on expanding deployment options, improving CI automation, and documenting contributions. Key feature deliveries and updates: - DoMINO: Optional dependencies support implemented to allow usage in environments without VTK/PyVista by wrapping imports in try-except and raising ImportError if unavailable. Commit: 22e6dd3e0cb7b2477326b6d07eb1c3e09daebf72. - CI Access Control Enhancement: Authorized new user rishikeshranade to trigger CI workflows in blossom-ci.yml, extending automated build/check capabilities. Commit: 1e3cab8b688596f31b645b3b03afe6ea8ac951bc. - Documentation: Contributor acknowledgement for 1d bloodflow added to README naming Luca Pegolotti and Stanford affiliation. Commit: 02219a5140189f856c5a608d0d8e18ea0b4c23cf. Major bugs fixed: None reported this month. The focus was on feature delivery and process improvements. Overall impact: Broader adoption potential for DoMINO across environments with optional dependencies; enhanced CI automation and contributor visibility; better onboarding and attribution for external contributors. Technologies/skills demonstrated: Python dependency management (optional imports), exception handling for optional deps, CI/CD configuration and permissions, project documentation and contributor acknowledgement, cross-team collaboration.
3 Commits • 3 Features
Jan 1, 2025January 2025 (Month: 2025-01) — NVIDIA/physicsnemo. Focused on expanding deployment options, improving CI automation, and documenting contributions. Key feature deliveries and updates: - DoMINO: Optional dependencies support implemented to allow usage in environments without VTK/PyVista by wrapping imports in try-except and raising ImportError if unavailable. Commit: 22e6dd3e0cb7b2477326b6d07eb1c3e09daebf72. - CI Access Control Enhancement: Authorized new user rishikeshranade to trigger CI workflows in blossom-ci.yml, extending automated build/check capabilities. Commit: 1e3cab8b688596f31b645b3b03afe6ea8ac951bc. - Documentation: Contributor acknowledgement for 1d bloodflow added to README naming Luca Pegolotti and Stanford affiliation. Commit: 02219a5140189f856c5a608d0d8e18ea0b4c23cf. Major bugs fixed: None reported this month. The focus was on feature delivery and process improvements. Overall impact: Broader adoption potential for DoMINO across environments with optional dependencies; enhanced CI automation and contributor visibility; better onboarding and attribution for external contributors. Technologies/skills demonstrated: Python dependency management (optional imports), exception handling for optional deps, CI/CD configuration and permissions, project documentation and contributor acknowledgement, cross-team collaboration.
January 2025
November 2024
1 Commits • 1 Features
Nov 1, 2024November 2024: Delivered XAeroNet, a scalable neural network framework for aerodynamic evaluation, including XAeroNet-S (surface) and XAeroNet-V (volume) models. Implemented model architectures, data processing scripts for surface and volume datasets, and training configurations using the DrivAerML dataset. This work enables faster, more accurate aero evaluations to inform design decisions and scales the modeling pipeline for additional configurations. No major bugs reported this month; focus was on feature delivery and establishing a reproducible ML pipeline that enhances design throughput and decision support across NVIDIA/physicsnemo.
November 2024
1 Commits • 1 Features
Nov 1, 2024November 2024: Delivered XAeroNet, a scalable neural network framework for aerodynamic evaluation, including XAeroNet-S (surface) and XAeroNet-V (volume) models. Implemented model architectures, data processing scripts for surface and volume datasets, and training configurations using the DrivAerML dataset. This work enables faster, more accurate aero evaluations to inform design decisions and scales the modeling pipeline for additional configurations. No major bugs reported this month; focus was on feature delivery and establishing a reproducible ML pipeline that enhances design throughput and decision support across NVIDIA/physicsnemo.
Activity
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Quality Metrics
Correctness91.6%
Maintainability89.6%
Architecture90.8%
Performance87.6%
AI Usage22.6%
Skills & Technologies
Programming Languages
BashC++MarkdownPythonShellYAML
Technical Skills
AWS S3Aerodynamics SimulationBug FixingCFDCI/CD ConfigurationCode OrganizationComputational Fluid Dynamics (CFD)Configuration ManagementDGLData EngineeringData LoadingData PreprocessingData ProcessingDebuggingDeep Learning
Repositories Contributed To
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