Sylvie Putot
Papers
3
Total Citations
28
H-Index
2
About
Sylvie Putot is a leading researcher at the intersection of machine learning, physics-informed modeling, and autonomous systems. Her work focuses on integrating fundamental physical principles—such as conservation laws and Jacobian constraints—into deep neural network architectures, enabling more data-efficient and generalizable models for dynamical systems. This approach, detailed in her highly cited 2021 paper (22 citations), represents a significant advance in bridging the gap between data-driven and physics-based modeling, with broad implications for engineering and scientific computing. Putot has also made notable contributions to autonomous underwater robotics, developing novel methods for estimating coverage and area exploration using side-scan sonar and line-sweep sensors. Her 2022 work (4 citations) addresses a critical challenge in path planning: ensuring complete area coverage by accurately measuring the terrain actually explored. With a growing body of work that spans both theoretical foundations and practical applications, Putot’s research is shaping how robots learn from and interact with complex physical environments.
Research Focus
Key Achievements
Top Papers
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