Scientific machine learning (SciML) is increasingly applied to in-field processing, controlling, and monitoring; however, wide-area sensing, real-time demands, and strict energy and reliability constraints make centralized SciML implementation impractical. Most SciML models assume raw data aggregation at a central node, incurring prohibitively high communication latency and energy costs; yet, distributing models developed for general-purpose ML often breaks essential physical principles, resulting in degraded performance. To address these challenges, we introduce EPIC, a hardware- and physics-co-guided distributed SciML framework, using full-waveform inversion (FWI) as a representative task. EPIC performs lightweight local encoding on end devices and physics-aware decoding at a central node. By transmitting compact latent features rather than high-volume raw data and by using cross-attention to capture inter-receiver wavefield coupling, EPIC significantly reduces communication cost while preserving physical fidelity. Evaluated on a distributed testbed with five end devices and one central node, and across 10 datasets from OpenFWI, EPIC reduces latency by 8.9$\times$ and communication energy by 33.8$\times$, while even improving reconstruction fidelity on 8 out of 10 datasets.

翻译：科学机器学习（SciML）正日益应用于现场处理、控制与监测；然而，广域传感、实时性需求以及严格的能量与可靠性约束使得集中式SciML实现变得不切实际。大多数SciML模型假设原始数据在中央节点聚合，这会产生极高的通信延迟与能量成本；而为通用机器学习开发的分布式模型往往破坏基本物理原理，导致性能下降。为应对这些挑战，我们提出了EPIC，一个硬件与物理协同引导的分布式SciML框架，并以全波形反演（FWI）作为代表性任务。EPIC在终端设备上执行轻量级本地编码，并在中央节点进行物理感知解码。通过传输紧凑的潜在特征而非大体积原始数据，并利用交叉注意力捕捉接收器间波场耦合，EPIC在保持物理保真度的同时显著降低了通信成本。在由五个终端设备与一个中央节点构成的分布式测试平台上，基于OpenFWI的10个数据集进行评估，EPIC将延迟降低了8.9$\times$，通信能量降低了33.8$\times$，甚至在10个数据集中的8个上提升了重建保真度。