Yanghao Chen

Yanghao Chen

Undergraduate student | Tongji UniversityComputational MechanicsScientific Machine Learning

Yanghao Chen

Hi! I’m Yanghao Chen, a final-year undergraduate at Tongji University, studying Engineering Mechanics in the School of Aerospace Engineering and Applied Mechanics. My interests center on scientific machine learning and computational mechanics.

My research began with fine-tuning large language models using LoRA on Google Colab for question answering in mechanics of materials. I then developed ConvLSTM models to predict lithium concentration and von Mises stress image sequences in battery materials using MATLAB–COMSOL simulation data. I also studied shock propagation with physics-informed neural networks (PINNs), comparing standard and artificial-viscosity formulations against the analytical inviscid solution.

Together, these experiences have shaped my interest in combining machine learning with numerical methods for scientific simulation. My current research focuses on neural operators, which learn mappings between function spaces, and hybrid FEM–neural operator solvers based on non-overlapping domain decomposition. I aim to combine AI-based models with GPU-accelerated numerical computing to make engineering simulations more efficient and scalable.

At Tongji, I have been fortunate to work under the guidance of Prof. Ying Zhao and Prof. Xianyang (Tom) Chen. From January to October 2026, I was an undergraduate research intern in the Department of Civil and Systems Engineering at Johns Hopkins University, working with Prof. Somdatta Goswami in Centrum IntelliPhysics.

News

Sep 2026
3D Composite Extension: Extending the hybrid FEM–neural operator solver to a 100-fiber composite. A shared neural operator computes stress fields across all fiber subdomains, with component-specific weights shared across fibers. Periodic boundary conditions eliminate fiber-end effects. View the composite mesh.
Aug 2026
3D Bracket Studies: Extended the workflow to 3D bracket problems, using GRFs to sample interface displacement fields and tractions on internal surfaces. Trained neural operators to predict subdomain stress fields from these inputs.
Jul 2026
Cylinder Studies: Developed a hybrid FEM–neural operator solver for a cylinder problem, using GRFs to sample displacement fields on both inner and outer arcs. Coupled the FEM and neural-operator subdomains through iterative exchange of interface forces and displacements.
Jul 2026
Transitioned to an on-site undergraduate research internship at Johns Hopkins University to continue working with Prof. Somdatta Goswami.
Nov 2025
Developed standard and artificial-viscosity PINNs for one-dimensional Burgers shock problems, with analytical-reference comparisons of the predicted solution profiles.
Jun 2025
Began developing a MATLAB–COMSOL data-generation workflow and separate ConvLSTM models for concentration and stress image prediction in battery materials.
Sep 2024
Started curating instruction data and developing an LLM fine-tuning workflow for question answering in mechanics of materials.

Current Research

I am developing GPU-accelerated hybrid FEM–neural operator solvers based on non-overlapping domain decomposition. FEM and neural-operator subdomains are connected through shared interfaces. Neural operators learn mappings from interface displacement fields to subdomain stress fields. Interface reaction forces computed from predicted stresses are transferred to the FEM solver. The FEM solver uses these forces to compute updated interface displacements and returns them to the neural operators for the next coupling iteration.

View Research Projects in CV.

Skills

  • Scientific Machine Learning: PyTorch, JAX, Neural Operators (Transolver, DeepONet), PINNs, ConvLSTM
  • Computational Mechanics: JAX-FEM, FEniCSx, Abaqus, COMSOL Multiphysics (LiveLink for MATLAB), Gmsh
  • Programming & Tools: Python, MATLAB, Linux, Git/GitHub, Matplotlib, Origin
  • LLM Fine-Tuning: Qwen2.5, LoRA, Instruction Dataset Curation
  • Languages: Mandarin Chinese (native), Cantonese, English

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