Advanced hybrid LSTM-transformer architecture for real-time multi-task prediction in engineering systems.

ABSTRACT: In the field of engineering systems-particularly in underground drilling and green stormwater management-real-time predictions are vital for enhancing operational performance, ensuring safety, and increasing efficiency. Addressing this niche, our study introduces a novel LSTM-transformer hybrid architecture, uniquely specialized for multi-task real-time predictions. Building on advancements in attention mechanisms and sequence modeling, our model integrates the core strengths of LSTM and Transformer architectures, offering a superior alternative to traditional predictive models. Further enriched with online learning, our architecture dynamically adapts to variable operational conditions and continuously incorporates new field data. Utilizing knowledge distillation techniques, we efficientl

SUBMITTER: Cao K 

PROVIDER: S-EPMC11322354 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature