A hybrid solver based on non-overlapping domain decomposition, with FEM and neural-operator subdomains connected through a shared interface. The two solvers exchange interface displacements and reaction forces iteratively across this interface. Uses GPU-accelerated JAX-FEM simulations to generate training data and trains Transolver in JAX to learn mappings from interface displacement fields to local stress fields.
Research details
Trains conditional ConvLSTM models in PyTorch to predict future sequences of concentration and stress fields in polycrystalline battery materials. Separate models for each field use past field images, grain-orientation maps, and C-rate inputs, with MATLAB–COMSOL simulation data and a combined MSE and SSIM loss.
Repository
Trains PINNs in PyTorch to predict shock propagation in the one-dimensional Burgers equation, using PDE-residual, initial-condition, and boundary-condition losses. Compares standard and constant-artificial-viscosity PINNs against the analytical inviscid solution to assess the predicted shock profiles.
Repository
Adapts Qwen2.5-7B for question answering and concept explanation in mechanics of materials through LoRA fine-tuning on Google Colab. Uses curated instruction–response examples from textbooks and technical literature, with datasets and fine-tuned models released on Hugging Face.
Hugging Face
Hugging Face