Genesis is a universal physics engine designed for robotics and physical AI simulation. It provides a unified multi-physics framework supporting rigid-body dynamics, finite element method (FEM), material point method (MPM), position-based dynamics (PBD), smoothed particle hydrodynamics (SPH), and incremental potential contact (IPC) coupling. The engine is GPU-accelerated via a custom compiler (Quadrants, forked from Taichi) and scales from single-CPU laptops to multi-GPU datacenters. It features differentiable simulation with gradient tape and manual backward passes, enabling end-to-end optimization through physics. The architecture follows a scene-entity-solver pattern with pydantic-based options for configuration, a plugin-based sensor system (cameras, IMU, tactile, lidar, etc.), and multiple rendering backends (rasterizer, raytracer, batch renderer). Robotics-specific features include URDF/MJCF/USD asset import, inverse kinematics, PD control, and reinforcement learning integration via rsl-rl. The codebase is well-structured with extensive tests, CI/CD pipelines, and a large set of examples covering manipulation, locomotion, deformables, fluids, and coupling scenarios. While the engine is primarily an application library, it also serves as a research platform for differentiable physics and novel simulation techniques.
PhiFlow is a Python library for differentiable physics simulation, primarily targeting fluid dynamics, particle systems, and PDE-based optimization. It provides a unified field abstraction (CenteredGrid, StaggeredGrid, PointCloud, Mesh) with automatic resampling and boundary handling, enabling seamless integration with machine learning workflows. The library supports multiple backends (NumPy, PyTorch, TensorFlow, JAX) and features a rich geometry module, physics solvers (advection, diffusion, incompressible flow, SPH, waves), and visualization tools. While its core abstractions are clean and well-tested, the codebase exhibits some technical drift due to deprecated API surface, large monolithic Dash-based web UI, and heavy reliance on the external phiml tensor library. Nonetheless, it is actively maintained with extensive test coverage and a growing research community.