1. PHYSICS-INFORMED MACHINE LEARNING

Physics-based compressive sensing for solving inverse problems with unknown boundary conditions

Yan, Z., & Lu, Y. (2025). Decomposed physics-based compressive sensing for inverse heat source detection under sparse measurements and uncertain boundary conditions. International Journal of Heat and Mass Transfer, 253, 127505.

Physics-based compressive sensing for monitoring physical fields in fused filament fabrication and selective laser melting processes

Lu, Y., & Wang, Y. (2021). Physics based compressive sensing to monitor temperature and melt flow in laser powder bed fusion. Additive Manufacturing, 47, 102304.
Lu, Y., & Wang, Y. (2018). Monitoring temperature in additive manufacturing with physics-based compressive sensing. Journal of manufacturing systems, 48, 60-70.
Lu, Y., Shevtshenko, E., & Wang, Y. (2021). Physics-based compressive sensing to enable digital twins of additive manufacturing processes. Journal of Computing and Information Science in Engineering, 21(3), 031009.

Finite-volume informed U-net for compressible flow prediction with sparse data under ill-conditions

Zhu, T., Si, B., Fu, L., & Lu, Y. (2026). SFVnet: Finite-volume informed U-net for compressible flow prediction with sparse data under ill-conditions. Journal of Computational Physics, 114696.
Zhu, T., Liu, D., & Lu, Y. (2025). Finite-volume physics-informed U-net for flow field reconstruction with sparse data. Journal of Computing and Information Science in Engineering, 25(7), 071004.

2. LATTICE STRUCTURAL OPTIMIZATION

Lattice strcutural optimization based on periodic surface modeling and mixed-integer Bayesian optimization

Lu, Y., & Wang, Y. (2022). Structural optimization of metamaterials based on periodic surface modeling. Computer Methods in Applied Mechanics and Engineering, 395, 115057.

3. BIOPRINTING

General bioprinting workflow

Customized printer for embedded printing

4. FAULT DIAGNOSIS

Multiscale channel attention-driven graph dynamic fusion network for fault diagnosis based on mechanical signals

Zhang, X., Liu, J., Huang, R., Hao, J., Qiao, Z., & Lu, Y. (2026). Plug-and-play graph reliability enhancement method for equipment state description under sparse information. Reliability Engineering & System Safety, 112593.
Zhang, X., Huang, R., Liu, J., Qiao, Z., & Lu, Y. (2026). Time–frequency constrained graph-level representation learning paradigm for real-time mechanical fault diagnosis. Journal of Intelligent Manufacturing, 1-26.
Zhang, X., Liu, J., Zhang, X., & Lu, Y. (2025). Self-supervised graph feature enhancement and scale attention for mechanical signal node-level representation and diagnosis. Advanced Engineering Informatics, 65, 103197.
Zhang, X., Liu, J., Zhang, X., & Lu, Y. (2024). Multiscale channel attention-driven graph dynamic fusion learning method for robust fault diagnosis. IEEE Transactions on Industrial Informatics, 20(9), 11002-11013.