Computing in the large: Scalable numerical solutions for forward and inverse problems in additive manufacturing

Date: 2022/11/30 - 2022/11/30

Academic Seminar: Computing in the large: Scalable numerical solutions for forward and inverse problems in additive manufacturing

Speaker: Dr. Tianju Xue, postdoctoral researcher at Northwestern University

Time: 9:00 a.m. Nov. 30, 2022 (Beijing Time)

Location: via Feishu


Additive manufacturing (AM) is a family of rising technologies that are well-known for their flexibility in fabricating geometrically and/or material-wisely complex parts that are difficult for traditional manufacturing processes. However, AM processes are often plagued with a lack of reproducibility and reliability. Computational modeling has become an appealing approach in understanding the Process-Structure-Property (PSP) relationship in AM processes, critical for process control and optimization.


My research work has focused on developing efficient and scalable numerical algorithms for solving both forward and inverse design problems in AM processes. In this presentation, I will first discuss the Process-Structure linkage in a laser-based powder bed fusion (PBF) process, where a new physics-embedded graph network method is proposed to accelerate the traditional phase-field simulation for material microstructure evolution. Then, I will introduce an open-source differentiable simulation platform with GPU acceleration that I initiated, called JAX-AM (, that provides full-stack analysis for AM processes. Finally, I will switch to the Structure-Property linkage and show AM-enabled design of structurally intricate mechanical metamaterials that possess unique mechanical properties, e.g., having a negative Poisson's ratio.


Tianju Xue is a postdoctoral researcher in mechanical engineering at Northwestern University working with Prof. Jian Cao. He earned his Ph.D. degree from Princeton University and his B.Eng. degree from UM-SJTU Joint Institute at Shanghai Jiao Tong University. Tianju has wide research interest, particularly at the intersection of computational science, mechanics, and additive manufacturing. His work has been published on journals like J. Comput. Phys. | Comput. Methods in Appl. Mech. Eng. | Int. J. Numer. Methods Eng., as well as machine learning conferences such as ICML | NeurIPS.