For a full list of publications see below or go to Google Scholar
Tran A., Sun J., Liu D., Wildey T., and Wang Y. (2022). Stochastic reduced-order model with temporal upscaling for uncertainty propagation in materials modeling. Journal of Computing and Information Science in Engineering (accepted).
Biswas, S., Liu, D., & Jiang, W. (2022). Solidification and grain formation in alloys: a 2D application of the grand-potential-based phase-field approach. Modelling and Simulation in Materials Science and Engineering, 30(2), 025013.
Liu D. and Wang Y. (2020) Multiphysics simulation of nucleation and grain growth in selective laser melting of alloys. Journal of Computing and Information Science in Engineering, 20(5).[Download PDF]
Sestito J.M., Liu D., Lu Y., Song J.-H., Tran A.V., Kempner M.J., Harris T.A.L., Ahn S.-H., and Wang Y. (2019) Multiscale process modeling of shape memory alloy fabrication with directed energy deposition. Additive Manufacturing for Multifunctional Materials and Structures, eds. by H. Bruck, Y. Chen, and S.K. Gupta.
Tran A.V., Liu D., He L., and Wang Y. (2019) Accelerating first-principle saddle point and local minimum search based on scalable Gaussian processes. Uncertainty Quantification in Multiscale Materials Modeling, eds. by Y. Wang and D.L. McDowell (Elsevier), Ch.5, pp.119-168.
Cao L., Liu D., Jiang P., Shao X., Zhou Q., and Wang Y. (2019). Multi-physics simulation of dendritic growth in magnetic field assisted solidification. International Journal of Heat and Mass Transfer, 144: 118673. [Download PDF]
Liu D. and Wang Y. Simulation of nucleation and grain growth in selective laser melting of Ti-6Al-4V alloy. Proceedings of 2019 ASME International Design Engineering Technical Conferences & Computers and Information in Engineering Conference (IDETC/CIE2019), August 18-21, 2019, Anaheim, California, Paper No. DETC2019-97684. [Download PDF]
Tran A. V., Liu D., Tran H. A., and Wang Y. (2019). Quantifying Uncertainty in the Process-Structure Relationship for Al-Cu Solidification. Modelling and Simulation in Materials Science and Engineering, 27(6): 064005. [Download PDF]
Liu D., and Wang Y. (2019). Mesoscale multi-physics simulation of rapid solidification of Ti-6Al-4V alloy. Additive Manufacturing, 25: 551-562. [Download PDF]
Nie Z., Wang G., Liu D., and Rong Y. K. (2018). A statistical model of equivalent grinding heat source based on random distributed grains. Journal of Manufacturing Science and Engineering, 140(5): 051016. [Download PDF]
Liu D., & Wang Y. Mesoscale Multi-Physics Simulation of Solidification in Selective Laser Melting Process Using a Phase Field and Thermal Lattice Boltzmann Model. Proceedings of 2017 ASME International Design Engineering Technical Conferences & Computers and Information in Engineering Conference (IDETC/CIE2017), Aug. 6-9, 2017, Cleveland, Ohio, Paper No. DETC2017-67633. [Download PDF]
Liu D., Wang G., Yu J., & Rong Y. K. (2017). Molecular dynamics simulation on formation mechanism of grain boundary steps in micro-cutting of polycrystalline copper. Computational Materials Science, 126: 418-425. [Download PDF]
Nie Z., Wang G., Yu J., Liu D., & Rong Y. K. (2016). Phase-based constitutive modeling and experimental study for dynamic mechanical behavior of martensitic stainless steel under high strain rate in a thermal cycle. Mechanics of Materials, 101: 160-169. [Download PDF]
Liu D., Wang G., Nie Z., & Rong Y. K. Numerical Simulation of the Austenitizing Process in Hypoeutectoid Fe-C Steels. Proceedings of the ASME 2014 International Manufacturing Science and Engineering Conference (MSEC2014), June 9-13, 2014, Detroit, Michigan, Paper No. MSEC2014-3948. [Download PDF]
Liu D. and Wang Y. (2022). Metal additive manufacturing process design based on physics constrained neural networks and multi-objective Bayesian optimization. Manufacturing Letter (accepted).
Liu, D., and Wang, Y. (2021). A Dual-Dimer method for training physics-constrained neural networks with minimax architecture. Neural Networks, 136: 112-125. [Download PDF]
Liu D., and Wang Y. (2019). Multi-Fidelity Physics-Constrained Neural Network and Its Application in Materials Modeling. Journal of Mechanical Design, 141(12): 121403. [Download PDF]
Liu D. and Wang Y. Multi-fidelity physics-constrained neural network and its application in materials modeling. Proceedings of 2019 ASME International Design Engineering Technical Conferences & Computers and Information in Engineering Conference (IDETC/CIE2019), August 18-21, 2019, Anaheim, California, Paper No. DETC2019-98115. [Download PDF]
Liu, D., Wang, G., Nie, Z., & Rong, Y. K. (2016). An in-situ infrared temperature-measurement method with back focusing on surface for creep-feed grinding. Measurement, 94: 645-652. [Download PDF][Copyright Notice: The downloadable publications on this page are presented to ensure timely dissemination of technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by authors' copyright. These works may not be reposted without the explicit permission of the copyright holder.]