Fast, GPU-native differentiable physics and beyond with Tesseract (docs.pasteurlabs.ai)

Tesseract has announced significant improvements with its latest version (v1.15.0), enabling fast GPU-native differentiable physics simulations inside JAX and PyTorch training loops. This update eliminates the previous bottleneck of data transfer between the GPU and host memory, allowing arrays to remain on the GPU throughout the computation process. By using a new GPU transport feature, Tesseract can now handle GPU arrays by sending only minimal handles across process boundaries, thereby reducing the time taken for communication by orders of magnitude—particularly impactful for large datasets typical in 3D simulations. This enhancement is crucial for the AI/ML community, especially for applications requiring real-time training with differentiable solvers, such as control and reinforcement learning tasks. The practical implications include faster training cycles for models like Multi-Agent-DPC, which optimizes control policies for swarms of mobile heaters. The integration of this advanced GPU transport paves the way for more efficient workflows in training AI models that interact with physical systems, showcasing how optimized data processing can significantly elevate the performance of machine learning applications.