Akshay Praveen Nair
Data science, analytics and engineering
Hometown: Tempe, Arizona, United States
Graduation date: Spring 2026
MORE | Spring 2025
Water Physics Simulation Using Fourier Neural Operators
Traditional fluid simulations are computationally expensive and struggle to scale efficiently, limiting their use in real-time applications such as gaming, virtual environments, and scientific hydrodynamic modeling. This research applies Fourier Neural Operators (FNO) to solve partial differential equations in the frequency domain, allowing for faster and more scalable simulations. FNOs eliminate the computational bottlenecks of traditional numerical methods, enabling high-fidelity fluid modeling at a fraction of the cost. The expected outcome is a high-precision, computationally efficient water simulation model capable of capturing complex fluid dynamics while maintaining scalability. The results could significantly impact fields such as environmental modeling, engineering simulations, and real-time graphics, improving both scientific understanding and practical applications.
Mentor: Kookjin Lee
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