研究模型架构对流体预测性能与碳排放的权衡，发现无模型兼具高效与低碳。
Performance-Carbon Trade-Offs across Architectural Biases in Shear Flow Forecasting
- 对比7种不同感受野和周期性假设的模型，分析其性能与碳排
- 训练成本低不等于推理碳排低，训练阶段碳排不能仅靠时长估算
- 强调全生命周期碳成本评估，建议将效率纳入模型设计核心
现代深度学习发展主要受提升模型有效性（准确率指标）驱动，导致大规模模型需大量计算资源，带来显著碳足迹。本文研究模型架构偏差（尤其是感受野和周期性假设）在不可压缩剪切流时空预测中，对预测性能与碳足迹权衡的影响。我们比较了7种具有不同特性的模型，评估其在训练与推理阶段的点精度、物理保真度及碳成本。结果表明：无模型在性能与碳排上全面领先；训练成本低并不直接转化为推理成本低；运行时间不能可靠反映碳成本，尤其在训练阶段。这些发现凸显了必须在整个模型生命周期中显式评估碳成本的重要性。我们主张，模型效率应与有效性并列，成为机器学习开发与部署的核心考量。
原文摘要 · Abstract (English)
Development of modern deep learning methods has been driven primarily by the push for improving model efficacy (accuracy metrics), leading to large-scale models that require massive computational resources and result in considerable carbon footprint across the model lifecycle. In this work, we explore how architectural biases, specifically a model's receptive field and periodicity assumption, are associated with the trade-offs between predictive performance and carbon footprint for spatio-temporal forecasting of incompressible shear flow. We study seven models differing in these properties and evaluate pointwise accuracy, physics-fidelity, and carbon cost across training and inference. We find that no single model dominates across both predictive performance and carbon cost, that a lower training cost does not straightforwardly extend to inference, and that runtime alone is an unreliable proxy for carbon cost, particularly during training. Together, these results underscore the importance of explicitly evaluating carbon costs across the full model lifecycle. We argue that model efficiency, alongside efficacy, should be a core consideration in machine learning model development and deployment.
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