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
Zach Corse developed a differentiable physics simulation example for the newton-physics/newton repository, focusing on a bear model controlled by a neural network with a center-of-mass momentum-based loss function. He implemented the example using C++ and Python, integrating machine learning techniques to enable gradient-based optimization within the simulation. Zach updated documentation and unit tests to support the new feature, improving onboarding and test reliability. His work emphasized portability and consistency by porting an existing tet bear diffsim example, laying a foundation for more accurate differentiable simulations. The depth of engineering addressed both research needs and maintainability for future development.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
1Total
Bugs
0
Commits
1
Features
1
Lines of code
338
Activity Months1
Your Network
27 people
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
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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)
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