newton-physics/newton
Oct 2025 – Oct 2025
1 Month active
Languages Used
C++Python
Technical Skills
C++Differentiable PhysicsMachine LearningPythonSimulationUSD (Universal Scene Description)
Zach Corse
PROFILE
Zach Corse
Developed a differentiable physics simulation example for the newton-physics/newton repository, focusing on integrating a neural network controller with a center-of-mass momentum-based loss function. The work leveraged C++ and Python to implement the simulation and controller, while also updating documentation and unit tests to support onboarding and ensure reliability. By porting the tet bear diffsim example into the new framework, the developer emphasized code reuse and consistency across projects. Maintenance efforts targeted portability and robust test coverage, enhancing the foundation for future differentiable simulation research. The project utilized skills in machine learning, simulation, and Universal Scene Description (USD).
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
1Total
Bugs
0
Commits
1
Features
1
Lines of code
338
Activity Months1
Your Network
57 peopleShared Repositories
57
Work History
October 2025
1 Commits • 1 Features
Oct 1, 2025Month: 2025-10 | Repository: newton-physics/newton. This month focused on delivering a tangible differentiable physics component and improving documentation and test coverage. Key accomplishments include the Bear Differentiable Physics Simulation Example with a neural network controller and center-of-mass momentum-based loss, along with updating docs and tests to include the new example. No major bugs reported; maintenance tasks focused on portability and test reliability. The work enhances research capabilities and lays groundwork for more accurate differentiable simulations in downstream projects.
1 Commits • 1 Features
Oct 1, 2025Month: 2025-10 | Repository: newton-physics/newton. This month focused on delivering a tangible differentiable physics component and improving documentation and test coverage. Key accomplishments include the Bear Differentiable Physics Simulation Example with a neural network controller and center-of-mass momentum-based loss, along with updating docs and tests to include the new example. No major bugs reported; maintenance tasks focused on portability and test reliability. The work enhances research capabilities and lays groundwork for more accurate differentiable simulations in downstream projects.
October 2025
Activity
Loading activity data...
Quality Metrics
Correctness80.0%
Maintainability80.0%
Architecture80.0%
Performance60.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
C++Python
Technical Skills
C++Differentiable PhysicsMachine LearningPythonSimulationUSD (Universal Scene Description)
Repositories Contributed To
Overview of all repositories you've contributed to across your timeline