Learning heat transfer at ultrafast scales

Research overview

In this project, I developed an artificial neural network (ANN) framework to simulate ultrafast heat conduction in double-layered metallic thin films subjected to ultrashort-pulsed laser heating. Traditional approaches rely on solving the parabolic two-temperature model (TTM), which captures the coupled dynamics between electron and lattice temperatures at micro/nanoscale time scales. Our ANN-based method offers a data-driven alternative capable of approximating the TTM solution with strong theoretical convergence guarantees. I contributed to the design and implementation of the ANN architecture and helped evaluate its performance against analytical solutions. The model was used to accurately predict the thermal responses in a gold-on-chromium thin film system under femtosecond laser excitation, demonstrating the potential of physics-informed ML for nanoscale thermal analysis in advanced materials and laser processing applications.