Events Calendar

Computational Science Distinguished Seminar Series

Speaker: Jessica Zhang, Carnegie Mellon University

Talk Title: From neurological disorders to additive manufacturing: integrating isogeometric analysis with deep learning and digital twins

Abstract: Coupling physics-based simulation and data-driven modeling have demonstrated great power in predicting complex systems. This talk focuses on integrating an advanced finite element method called isogeometric analysis (IGA) with deep learning and digital twins to address challenging problems in investigating neurological disorders and additive manufacturing (AM). To investigate neurodevelopmental disorders, we introduce a novel phase field model coupled with tubulin and synaptogenesis concentration to simulate intricate neurite outgrowth and disorders using IGA, dynamic domain expansion and local refinement. By integrating IGA and convolutional neural networks, we conduct thorough investigations into the functional role of various parameters affecting the neurodevelopmental disorder with comparison to experimental results. To investigate intracellular transport induced neurodegenerative disorders, we develop a PDE-constrained optimization model to simulate traffic jams induced by microtubule reduction and swirl. We also build a novel IGA-based physics-informed graph neural network to quickly predict normal and abnormal transport phenomena in complex neuron geometries.
 
In the second half of the talk, I will present our latest research on generative manufacturing or combining AI with IGA for AM applications. It includes a machine learning framework for inverse design and manufacturing of self-assembling fiber-reinforced composites in 4D printing, IGA-based topology optimization for AM of heat exchangers, as well as data-driven residual deformation prediction to enhance metal component printability and lattice support structure design in the laser powder bed fusion (LPBF) AM process. By speeding up geometry distortion predictions from several hours to mere seconds with uncertainty quantification, our model can be deployed to prevent generation of infeasible designs. Our on-going efforts also include developing digital twins to enable prediction and control of process parameters in LPBF manufacturing, where reduced order modeling is one key technique to efficiently simulate underlying physics.
 

Biography: Jessica Zhang is the George Tallman Ladd and Florence Barrett Ladd Professor of Mechanical Engineering at Carnegie Mellon University with a courtesy appointment in Biomedical Engineering. She received her B.Eng. in Automotive Engineering, and M.Eng. in Engineering Mechanics from Tsinghua University, China; and M.Eng. in Aerospace Engineering and Engineering Mechanics and Ph.D. in Computational Engineering and Sciences from Institute for Computational Engineering and Sciences (now Oden Institute), The University of Texas at Austin. Her research interests include computational geometry, isogeometric analysis, finite element method, data-driven simulation, image processing, and their applications in computational biomedicine and engineering. Zhang has co-authored over 230 publications in peer-reviewed journals and conference proceedings and received several Best Paper Awards. She published a book entitled “Geometric Modeling and Mesh Generation from Scanned Images” with CRC Press, Taylor & Francis Group. Zhang is the recipient of Simons Visiting Professorship from Mathematisches Forschungsinstitut Oberwolfach of Germany, US Presidential Early Career Award for Scientists and Engineers, NSF CAREER Award, Office of Naval Research Young Investigator Award, and USACM Gallagher Young Investigator Award. At CMU, she received David P. Casasent Outstanding Research Award, George Tallman Ladd and Florence Barrett Ladd Professorship, Clarence H. Adamson Career Faculty Fellow in Mechanical Engineering, Donald
L. & Rhonda Struminger Faculty Fellow, and George Tallman Ladd Research Award. She is a Fellow of ASME, SIAM, IACM, USACM, IAMBE, AIMBE, SMA, and ELATES at Drexel. She is the Editor-in-Chief of Engineering with Computers.

Host: The School of Advanced Computing

More Info: https://sac.usc.edu/events/

Location: Ronald Tutor Hall of Engineering (RTH) - 526

Audiences: Everyone Is Invited

Contact: Tessa Yao

Event Link: https://sac.usc.edu/events/