Recently, the U.S. Department of Energy (DOE), Office of Science, Biological and Environmental Research (BER), and Advanced Scientific Computing Research (ASCR) programs organized and held the Artificial Intelligence for Earth System Predictability (AI4ESP) workshop series. From this workshop, a critical conclusion that the DOE BER and ASCR community came to is the requirement to develop a new paradigm for Earth system predictability focused on enabling artificial intelligence (AI) across the field, lab, modeling, and analysis activities, called ModEx. The BER's `Model-Experimentation', ModEx, is an iterative approach that enables process models to generate hypotheses. The developed hypotheses inform field and laboratory efforts to collect measurement and observation data, which are subsequently used to parameterize, drive, and test model (e.g., process-based) predictions. A total of 17 technical sessions were held in this AI4ESP workshop series. This paper discusses the topic of the `AI Architectures and Co-design' session and associated outcomes. The AI Architectures and Co-design session included two invited talks, two plenary discussion panels, and three breakout rooms that covered specific topics, including: (1) DOE HPC Systems, (2) Cloud HPC Systems, and (3) Edge computing and Internet of Things (IoT). We also provide forward-looking ideas and perspectives on potential research in this co-design area that can be achieved by synergies with the other 16 session topics. These ideas include topics such as: (1) reimagining co-design, (2) data acquisition to distribution, (3) heterogeneous HPC solutions for integration of AI/ML and other data analytics like uncertainty quantification with earth system modeling and simulation, and (4) AI-enabled sensor integration into earth system measurements and observations. Such perspectives are a distinguishing aspect of this paper.

翻译：近期，美国能源部（DOE）科学办公室下属的生物与环境研究（BER）及先进科学计算研究（ASCR）项目联合组织了"人工智能驱动地球系统可预测性"（AI4ESP）系列研讨会。该研讨会得出的重要结论指出，DOE的BER和ASCR社区需要建立一种聚焦于在实地观测、实验室研究、建模与分析活动中全面应用人工智能（AI）的新型地球系统可预测性范式，即ModEx方法。BER倡导的"模型-实验"（ModEx）是一种迭代方法，使过程模型能够生成科学假说，这些假说进而指导实地与实验室工作以采集测量与观测数据，最终用于参数化、驱动和检验（如基于过程的）模型预测。本次AI4ESP系列研讨共设17个技术分论坛，本文聚焦"人工智能架构与协同设计"分论坛的议题及成果。该分论坛包含两场特邀报告、两次全体讨论会及三个专题分组讨论，分别涵盖：（1）DOE高性能计算（HPC）系统，（2）云端HPC系统，（3）边缘计算与物联网（IoT）。本文进一步提出通过与其他16个分论坛主题协同，可在该协同设计领域开展的前瞻性研究构想与视角，包括：（1）重构协同设计理念，（2）数据采集到分发全链条优化，（3）面向AI/ML与不确定性量化等数据分析方法融合地球系统建模与仿真的异构HPC解决方案，（4）基于AI的传感器集成融入地球系统测量与观测。这些前瞻性视角构成了本文的独特价值。