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NeuralOperator is an open-source PyTorch library that implements neural operator architectures (notably Fourier Neural Operators) for learning mappings between function spaces. It targets physics-informed tasks such as PDE modeling, provides resolution-invariant operators, tensorized (Tucker) variants for parameter efficiency, and ready-to-use training and examples.

Warp is a high-performance Python framework from NVIDIA for writing JIT-compiled kernels that run on CPU or CUDA GPUs. It focuses on simulation, spatial computing and data generation; kernels are differentiable and integrate with ML frameworks such as PyTorch and JAX. Warp includes many primitives, example notebooks, and pre-built packages distributed via PyPI and GitHub Releases.

Isaac Lab is an open-source, GPU-accelerated robotics learning framework built on NVIDIA Isaac Sim. It provides high-fidelity physics and sensor simulation, ready-to-train environments and robot models, and integrations for reinforcement and imitation learning workflows to accelerate sim-to-real research and large-scale robot training.

Genesis is an open-source physics and simulation platform for general-purpose robotics and embodied AI. It integrates multiple physics solvers, photo-realistic ray-tracing rendering, and a generative data engine. Designed for extreme speed, cross-platform use, and differentiable simulation, Genesis targets robotics research, automated dataset generation, and simulation-driven AI development.

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.