I am Tsung-Yeh Hsieh, a Ph.D. student in Mechanical Engineering at Carnegie Mellon University, working in the Computational Bio-Modeling Lab advised by Prof. Jessica Zhang.

My research interests include computational mechanics, scientific machine learning, AI for science, AI for PDEs, numerical analysis, multiscale modeling, and meshfree methods. Before joining CMU, I received my M.S. and B.S. degrees from National Tsing Hua University, where I worked on physics-informed neural networks, reduced-order modeling, and multiscale material simulation.

🔥 News

2025.09: Passed the Ph.D. qualifying exam at Carnegie Mellon University.
2025.07: Presented autoencoder-based surrogate modeling work at the 18th U.S. National Congress on Computational Mechanics in Chicago and received a Travel Award.
2024.08: Started Ph.D. study in Mechanical Engineering at Carnegie Mellon University.
2024.05: Presented shock wave modeling work at the Engineering Mechanics Institute Conference and Probabilistic Mechanics & Reliability Conference in Chicago.
2023.10: Received Third Place in the Student Paper Competition at the NCFD Conference, Taiwan.
2023.08: Presented advection-dominated flow modeling work at the 28th National Computational Fluid Dynamics Conference in Taipei, Taiwan.
2023.07: Presented numerically enhanced PINN work at the 17th U.S. National Congress on Computational Mechanics in Albuquerque, New Mexico.

📝 Publications

SIAM News 2026

Digital twins of neurons publication thumbnail

Decoding the Neural Enigma: Digital Twins of Neurons Revolutionize Brain Research

T.Y. Hsieh, A. Aldirany, J. Zhang

SIAM News, 2026

Discussed digital twins of neurons and their role in computational brain research.
[Article]

CMAME 2025

GALDS graph-autoencoder surrogate model thumbnail

GALDS: A Graph-Autoencoder-Based Latent Dynamics Surrogate Model to Predict Neurite Material Transport

T.Y. Hsieh, Y.J. Zhang

Computer Methods in Applied Mechanics and Engineering, 2025

Developed a graph-autoencoder-based latent dynamics surrogate model for neurite material transport prediction.
[Paper]

Engineering with Computers 2024

PINN weak boundary conditions publication thumbnail

A Multiscale Stabilized Physics-Informed Neural Networks with Weakly Imposed Boundary Conditions Transfer Learning Method for Modeling Advection-Dominated Flow

T.Y. Hsieh, T.H. Huang

Engineering with Computers, 2024

Developed a stabilized PINN formulation for advection-dominated flow problems with weak boundary-condition enforcement and transfer learning.
[Paper]

JMRT 2024

Neural-network-enhanced FEM TPMS publication thumbnail

An Efficient Parameterized Neural Network Enhanced Multiscale Finite Element Modeling for Triply Periodic Minimal Surface Meta-Structures and its Applications for Femur

Y.Z. Chen, C.H. Wang, T.Y. Hsieh, C.C. Tung, P.Y. Chen, T.H. Huang

Journal of Materials Research and Technology, 2024

Contributed to neural-network-enhanced multiscale finite element modeling for TPMS meta-structures and biomedical applications.
[Paper]

Full and automatically updated publication metrics are available on Google Scholar.

Conference Papers and Presentations

A Multi-Dimensional Framework for Efficient Material Transport Simulation in Complex Neurite Network Using Autoencoder-Based Surrogate Models, T.Y. Hsieh, Y.J. Zhang. 18th U.S. National Congress on Computational Mechanics, Chicago, Illinois, Jul. 2025.
A Space-Time Modularized Neural Network Approach for Shock Wave Modeling, T.Y. Hsieh, Y.M. Tsai, T.H. Huang. Oral presentation, Engineering Mechanics Institute Conference and Probabilistic Mechanics & Reliability Conference, Chicago, Illinois, May 2024.
Shock Wave Modeling with Enhanced Physics-Informed Neural Networks, T.Y. Hsieh, Y.M. Tsai, T.H. Huang. Oral presentation, Conference on Theoretical and Applied Mechanics, Yunlin, Taiwan, Nov. 2023.
An Enhanced Physics Informed Neural Networks for Shock Wave Modeling, T.Y. Hsieh, Y.M. Tsai, T.H. Huang. Oral presentation, Association of Computational Mechanics Taiwan Annual Meeting, Keelung, Taiwan, Oct. 2023.
Application of Artificial Neural Network Formulation for Advection Dominated Fluid Flow Problems, T.Y. Hsieh, T.H. Huang, Y.M. Tsai. Presented by Y.M. Tsai on behalf of T.Y. Hsieh, 28th National Computational Fluid Dynamics Conference, Taipei, Taiwan, Aug. 2023.
Numerically Enhanced Physics Informed Neural Network for Fluid Flow Problems, T.Y. Hsieh, T.H. Huang. Presented by T.H. Huang on behalf of T.Y. Hsieh, 17th U.S. National Congress on Computational Mechanics, Albuquerque, New Mexico, Jul. 2023.
Artificial Neural Network Methods for Advection Diffusion Problems, T.Y. Hsieh, T.H. Huang. Oral presentation, Conference on Theoretical and Applied Mechanics, Kaohsiung, Taiwan, Nov. 2022.
A Neural Network Enhanced Finite Element Method for TPMS Based Mechanical Metamaterials Simulation, Y.Z. Chen, T.Y. Hsieh, T.H. Huang, C.C. Tung, P.Y. Chen. Oral presentation, WCCM/APCOM, Yokohama, Japan, Jul.-Aug. 2022.
Deep Energy Method: A Neural Network Based Meshfree Solver for Hyperelastic Material, T.Y. Hsieh, T.H. Huang. Oral presentation, Conference on Theoretical and Applied Mechanics, virtual format, Nov. 2021.

🎖 Honors and Awards

2025.07: Travel Award, 18th U.S. National Congress on Computational Mechanics, Chicago, Illinois.
2023.10: Third Place Award, Student Paper Competition, NCFD Conference, Taiwan.
2022.12: Top Quarter Award, AI Cup Competition, Ministry of Education, Taiwan.
2022.12: Honorable Mention Award, Student Poster Competition, TSFD Conference, Taiwan.
2020.12: Honorable Mention Award, Capstone Project Competition, PME Department, NTHU, Taiwan.
2019.08: 9th Place Award, Formula SAE Japan, SAE International, as a team award.

📖 Education

2024 - Present: Ph.D. in Mechanical Engineering, Carnegie Mellon University. Computational Bio-Modeling Lab, advisor: Prof. Jessica Zhang.
2021 - 2023: M.S. in Power Mechanical Engineering, National Tsing Hua University. GPA: 4.08/4.3. Advisor: Prof. Tsung-Hui (Alex) Huang.
2017 - 2021: B.S. in Power Mechanical Engineering, National Tsing Hua University. Electrical and Control Division, advisor: Prof. J. Andrew Yeh.

🔬 Projects

A Novel Digital Twin for Optimizing Normothermic VCA Preservation: Developed FEM simulation solvers and a CT-to-mesh pipeline for cardiovascular flow simulation, and designed a digital twin workflow for VCA machine control and reinforcement-learning training.
Liver Volumetric Engineering: Developed FEM simulation solvers and CT-to-mesh workflows for cardiovascular and organ-scale flow simulation.
Data-Driven Morphological Growth and Material Transport Regulation for Biological Neural Circuit Design and Prediction: Developed machine-learning methods for neuron image segmentation and tracking, and trained transformer-based models for time-series biological data prediction.
Machine Learning for Computational Fluid Mechanics involving Strong Advection and Discontinuity: Developed advanced PINN methods for CFD, including weakly imposed boundary conditions, multiscale loss functions, modular shock-capturing networks, and GPU/CPU-parallelized meshfree solvers.
Physics-Informed Neural Network Approach for Anomaly Detection in Structural Problems: Implemented hybrid constitutive artificial neural networks and damage-based neural networks for crack detection and anomaly detection in hyperelastic materials.
Reduced-Order Modeling for Machine-Learning-Controlled PDEs: Integrated differentiable physical simulation with neural-network control for soft robotics and applied POD to structural mechanics problems.
Multiscale Porous and Composite Materials: Collaborated with materials science researchers on neural-network-based multiscale homogenization and micro-to-macro fracture and damage projection.

💻 Experience and Skills

2017 - 2024: Research Assistant, National Tsing Hua University. Managed and participated in interdisciplinary research projects and helped maintain Linux and Windows research servers.
2022: Teaching Assistant, Mechanics of Materials. Served as TA team leader; student rating: 4.9/5.0.
2020 Season: Head of Power Mechanical Group, NTHU Racing Team. Led electric race car reducer, timing system, and high-voltage charger projects.
Programming and Tools: Python, MATLAB, C/C++, Java, FEniCS, Docker, Git, Arduino, STM32, ANSYS, Inventor, SolidWorks, ADAMS, and ParaView.