Arjun Mani
Papers
1
Total Citations
2
H-Index
1
About
Arjun Mani is a rising researcher in computer graphics and physics-based simulation, with a focus on learning-driven fluid dynamics. His work bridges the gap between data-driven methods and physical accuracy, enabling fast, differentiable fluid simulators that generalize to novel surfaces—a critical challenge for applications in design, virtual environments, and robotics. His most-cited paper, "SurfsUp: Learning Fluid Simulation for Novel Surfaces" (2023), introduces a framework that models fluid mechanics on surfaces unseen during training, overcoming a key limitation of prior learning-based approaches. Though early in his career, with 2 citations to date, Mani’s contributions are already recognized for their potential to transform interactive simulation and automated design. His research stands out for its emphasis on generalization and physical plausibility, offering a path toward more robust and adaptable simulators. As the field increasingly demands real-time, accurate fluid modeling, Mani’s work positions him as an emerging voice in the integration of machine learning with classical simulation techniques.
Research Focus
Key Achievements
Top Papers
- 1SurfsUp: Learning Fluid Simulation for Novel Surfaces2 citations · 2023