Victor Scott Prieto Ruiz
Papers
2
Total Citations
3
H-Index
1
About
Victor Scott Prieto Ruiz is a rising researcher at the forefront of robotic environmental sensing and disaster response. His work centers on the critical challenge of **gas source localization and airborne material dispersion modeling**, particularly for Chemical, Biological, Radiological, or Nuclear (CBRN) incidents. His major contribution lies in pioneering **physics-guided machine learning** to solve complex inverse problems. In his most cited work (2024, 2 citations), he introduced a novel method combining **Physics-Guided Neural Networks** with **Sparse Bayesian Learning** and Poisson's equation, enabling a team of robots to cooperatively and accurately pinpoint gas leaks using sparse sensor data. This approach bridges the gap between theoretical physics and data-driven robotics. Complementing this, his experimental study (2024, 1 citation) provides crucial **wind tunnel parameter identification and analysis**, offering validated data essential for both computational fluid dynamics and robotic olfactory models. By fusing rigorous physical principles with advanced AI, Ruiz is laying the groundwork for faster, more reliable autonomous systems that can save lives in hazardous environments, making him a notable innovator in the field of robotic olfaction and environmental monitoring.
Research Focus
Key Achievements
Top Papers
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