提出碳排放指标EcoL2，平衡神经PDE求解器的精度与环保性。
Beyond Accuracy: EcoL2 Metric for Sustainable Neural PDE Solvers
- 引入EcoL2指标，综合评估训练与部署中的碳排放
- 实验验证其能有效衡量不同架构的环境成本
- 适合关注可持续科学计算的研究者与工程师
现实世界系统（如航空航天、铁路工程）常以偏微分方程（PDE）建模。准确估算此类系统的解至关重要。近年来，基于深度学习的神经PDE求解器成为可靠求解方法。然而当前研究主要聚焦于提升精度，忽视了过度计算带来的碳排放问题。本文提出一种针对PDE求解器的碳排放度量标准——EcoL2，该指标在数据收集、模型训练与部署全周期中平衡模型精度与碳排放。在物理信息机器学习与算子学习架构上的实验表明，EcoL2可全面评估模型性能与环境代价。随着这类求解器规模扩大与广泛应用，EcoL2为构建低长期环境影响的高性能科学机器学习系统迈出关键一步。
原文摘要 · Abstract (English)
Real-world systems, from aerospace to railway engineering, are modeled with partial differential equations (PDEs) describing the physics of the system. Estimating robust solutions for such problems is essential. Deep learning-based architectures, such as neural PDE solvers, have recently gained traction as a reliable solution method. The current state of development of these approaches, however, primarily focuses on improving accuracy. The environmental impact of excessive computation, leading to increased carbon emissions, has largely been overlooked. This paper introduces a carbon emission measure for a range of PDE solvers. Our proposed metric, EcoL2, balances model accuracy with emissions across data collection, model training, and deployment. Experiments across both physics-informed machine learning and operator learning architectures demonstrate that the proposed metric presents a holistic assessment of model performance and emission cost. As such solvers grow in scale and deployment, EcoL2 represents a step toward building performant scientific machine learning systems with lower long-term environmental impact.
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