Provides a scalable physics-and-rendering simulation interface for robotics and embodied-AI research — unified multi-physics solvers, the Nyx renderer, and the Quadrants compiler. Runs from laptop to datacenter GPUs; suited for sensor-rich data generation and RL/robotics prototyping.
Simulates diverse physical materials and robots with unified solvers (rigid, MPM, SPH, FEM, PBD), ray-traced rendering, differentiable components, and a natural-language driven data generator — optimized for large-scale embodied-AI research and synthetic-data pipelines.
GPU-accelerated physics simulation engine for robotics and simulation research — built on NVIDIA Warp with MuJoCo Warp backend, offering differentiable simulation, OpenUSD support, and extensions for RL/embodied-AI workflows. ([github.com](https://github.com/newton-physics/newton))
Multimodal STEM problem set for verifiable, answer-supervised training and RL: contains single-image, multi-panel, and multi-image PhD-level questions across physics, math, chemistry and biology. Each example has a deterministic ground-truth answer, enabling reward modeling and automated evaluation.
This paper proposes a quantitative framework for the rise-and-fall trajectory of complexity in closed systems, showing that a coffee-and-cream cellular automaton exhibits a bell-curve of apparent complexity when particles interact, thereby linking information theory with thermodynamics and self-organization.
