Japan
I am a mechanical engineering master's student at Doshisha University, focused on computational fluid dynamics (CFD) and physics-informed machine learning. My research compares CFD simulations of unsteady flow around a circular cylinder with predictions from Physics-Informed Neural Networks (PINNs), exploring how machine learning can reduce computation time while preserving physical accuracy. I work primarily with OpenFOAM, MATLAB, and Python. I am passionate about bridging classical numerical simulation and modern machine learning to make engineering analysis faster and more practical. I also enjoy building and sharing tools with the community, and maintain an open-source project that organizes CFD and OpenFOAM knowledge and workflows. I am especially interested in applying simulation and data-driven methods to real-world manufacturing and product development, and I am currently looking for internship opportunities where I can contribute these skills while learning how advanced technology is brought to production at scale.