Earth system predictability is challenged by the complexity of environmental
dynamics and the multitude of variables involved. Current AI foundation models,
although advanced by leveraging large and heterogeneous data, are often
constrained by their size and data integration, limiting their effectiveness in
addressing the full range of Earth system prediction challenges. To overcome
these limitations, we introduce the Oak Ridge Base Foundation Model for Earth
System Predictability (ORBIT), an advanced vision-transformer model that scales
up to 113 billion parameters using a novel hybrid tensor-data orthogonal
parallelism technique. As the largest model of its kind, ORBIT surpasses the
current climate AI foundation model size by a thousandfold. Performance scaling
tests conducted on the Frontier supercomputer have demonstrated that ORBIT
achieves 230 to 707 PFLOPS, with scaling efficiency maintained at 78% to 96%
across 24,576 AMD GPUs. These breakthroughs establish new advances in AI-driven
climate modeling and demonstrate promise to significantly improve the Earth
system predictability.

ORBIT 是一种先进的视觉转换模型，具有 1130 亿个参数，通过创新的张量数据正交并行技术实现扩展。在 Frontier 超级计算机上进行的性能扩展测试表明，ORBIT 实现了 230 到 707 PFLOPS 的性能，并且在 24,576 个 AMD GPU 上保持了 78% 到 96% 的扩展效率。这些突破为基于 AI 的气候建模带来了新的进展，并展示了显著提高地球系统可预测性的前景。

ORBIT: 橡树岭基础地球系统可预测模型

ORBIT: Oak Ridge Base Foundation Model for Earth System Predictability

We suggest a new algorithm to estimate representations of compact Lie groups
from finite samples of their orbits. Different from other reported techniques,
our method allows the retrieval of the precise representation type as a direct
sum of irreducible representations. Moreover, the knowledge of the
representation type permits the reconstruction of its orbit, which is useful to
identify the Lie group that generates the action. Our algorithm is general for
any compact Lie group, but only instantiations for SO(2), T^d, SU(2) and SO(3)
are considered. Theoretical guarantees of robustness in terms of Hausdorff and
Wasserstein distances are derived. Our tools are drawn from geometric measure
theory, computational geometry, and optimization on matrix manifolds. The
algorithm is tested for synthetic data up to dimension 16, as well as real-life
applications in image analysis, harmonic analysis, and classical mechanics
systems, achieving very accurate results.

我们提出了一种新的算法，用于从其轨道的有限样本中估计紧致 Lie 群的表示。与其他报道的技术不同，我们的方法允许直接作为不可约表示的直和来检索准确的表示类型。而且，对表示类型的了解可以重建其轨道，从而有助于确定生成该动作的 Lie 群。我们的算法适用于任何紧致 Lie 群，但仅考虑了 SO (2)，T^d，SU (2) 和 SO (3) 的具体实例。基于 Hausdorff 距离和 Wasserstein 距离的鲁棒性的理论保证已被导出。我们的工具是从几何测度理论、计算几何和矩阵流形上的优化中提取的。该算法在高达 16 维的合成数据以及图像分析、谐波分析和经典力学系统的实际应用中进行了测试，取得了非常精确的结果。

LieDetect：从点云中检测紧致李群的表示轨道

LieDetect: Detection of representation orbits of compact Lie groups from  point clouds

We present ORBIT, a unified and modular framework for robot learning powered
by NVIDIA Isaac Sim. It offers a modular design to easily and efficiently
create robotic environments with photo-realistic scenes and fast and accurate
rigid and deformable body simulation. With ORBIT, we provide a suite of
benchmark tasks of varying difficulty -- from single-stage cabinet opening and
cloth folding to multi-stage tasks such as room reorganization. To support
working with diverse observations and action spaces, we include fixed-arm and
mobile manipulators with different physically-based sensors and motion
generators. ORBIT allows training reinforcement learning policies and
collecting large demonstration datasets from hand-crafted or expert solutions
in a matter of minutes by leveraging GPU-based parallelization. In summary, we
offer an open-sourced framework that readily comes with 16 robotic platforms, 4
sensor modalities, 10 motion generators, more than 20 benchmark tasks, and
wrappers to 4 learning libraries. With this framework, we aim to support
various research areas, including representation learning, reinforcement
learning, imitation learning, and task and motion planning. We hope it helps
establish interdisciplinary collaborations in these communities, and its
modularity makes it easily extensible for more tasks and applications in the
future. For videos, documentation, and code: this https URL.

ORBIT 是一个统一的、模块化的机器人学习框架，由 NVIDIA Isaac Sim 提供支持。它采用模块化设计，可以轻松、高效地创建具有照片级真实场景和快速、精确刚性和可变形体模拟的机器人环境。它还提供了各种难度的基准任务，可以支持各种研究领域，包括表示学习、强化学习、模仿学习和任务规划等。