Condition-Specific Weighted Data Sparsification for Compute-Efficient Physical-System Regression

Industrial physical-system models are often retrained from dense datasets collected across various operating conditions. Full-data retraining can incur substantial cloud-side computation to process redundant observations, whereas aggressive pruning may discard rare or difficult states. This work introduces a condition-specific data sparsification framework for cost-constrained industrial machine learning on physical-system regression tasks. The framework formulates sparsification as a condition-specific weighted empirical approximation. Geometric representatives define the retained support, assignment counts preserve the empirical mass of omitted observations, and residual feedback corrects task-relevant omissions under a condition-specific budget. The procedure operates at the training-support level, leaving the predictor architectures and forward computations unchanged. The framework is evaluated on battery state-of-charge (SOC) estimation and gas-turbine NO${}_x$ prediction using paired model-initialization seeds. The reported analytical cloud-side FLOP counts cover the modeled data-selection, retraining, and validation computations. The final retention ratios are 39.2%–41.4% for SOC and 38.7%–41.7% for NO${}_x$. In the retention-matched battery MLP experiments, the framework obtains lower mean MAE than Random in two of four driving cycles and than GradMatch in three of four cycles, while using 10.1%–16.2% fewer mean analytical cloud-side FLOPs than GradMatch. Across all settings, seed-averaged FLOP reductions are 8.3%–89.2% for SOC and 20.0%–85.1% for NO${}_x$, while setting-level, seed-averaged relative MAE changes range from -9.8% to +18.0% and from -0.9% to +13.4%, respectively. These results characterize when condition-specific data sparsification is beneficial and support the selection of retention budgets for cost-constrained industrial retraining.