A multi-task learning model for fast prediction of flow characteristics in deterministic lateral displacement devices

Deterministic lateral displacement (DLD) devices have shown considerable promise in various applications but require further optimization to meet the demands of high-throughput processing and complex sample adaptability. Recent studies utilizing single-task learning (STL) within a machine learning framework have achieved initial success in predicting critical diameters in DLD systems, offering significant gains in efficiency and cost-effectiveness compared to traditional microfluidic experiments and numerical simulations. However, the inability of STL models to effectively capture inter-task relationships and shared physical features limits both prediction accuracy and generalization capability. To address these limitations, this study proposes a multi-task learning (MTL)-based deep learning framework for the simultaneous prediction of key flow characteristics in DLD devices, including inter-pillar flow fields and streamwise velocity profiles along decision lines. The method employs Bézier curves to generate diverse pillar geometries, with high-quality labeled data acquired through dissipative particle dynamics simulations. A shared-feature neural network is constructed to enable joint modeling of multiple flow-related objectives. The results indicate that the proposed MTL model not only achieves high-prediction accuracy but also substantially improves data efficiency and model generalizability. Compared to conventional STL approaches, the MTL framework allows for the parallel inference of multiple tasks within milliseconds using only images of the pillar array structures, without relying on additional physical assumptions or geometric simplifications. Overall, this study presents a MTL model for the intelligent design of DLD devices and extends the applicability of MTL in microscale flow modeling.