466 citations
- United States ArmyUS21 papers
- United States Army Corps of EngineersUS11 papers
- Coastal and Hydraulics LaboratoryUS10 papers
- University of VirginiaUS7 papers
- University of ArizonaUS5 papers
- Carnegie Mellon UniversityUS4 papers
- Landau Institute for Theoretical PhysicsRU4 papers
- Northeastern UniversityUS4 papers
- University of Illinois Urbana-ChampaignUS4 papers
- DEVCOM Army Research LaboratoryUS3 papers
- P.N. Lebedev Physical Institute of the Russian Academy of SciencesRU3 papers
- Stanford UniversityUS3 papers
46 papers
Underwater Dead Reckoning with Deployable Situation-Triggered Covariance Scheduling
Akshay Naik, Ramavarapu S. Sreenivas, Dustin Nottage +1
Underwater dead reckoning estimates vehicle position when vision is unavailable and external positioning cannot be assumed. A single set of filter parameters can work well in many…
A Multi-Tiered Bayesian Network Coastal Compound Flood Analysis Framework
Ziyue Liu, Meredith L. Carr, Norberto C. Nadal-Caraballo +3
Coastal compound floods (CCFs) are triggered by the interaction of multiple mechanisms, such as storm surges, storm rainfall, tides, and river flow. These events can bring signific…
Assessing the Robustness and Resilience of U.S. Strategic Highways: A Network Science Perspective
Sukhwan Chung, Daniel Sardak, Jeffrey Cegan +1
Network science is a powerful tool for analyzing transportation networks, offering insights into their structures and enabling the quantification of resilience and robustness. Unde…
MoistNet: Machine Vision-based Deep Learning Models for Wood Chip Moisture Content Measurement
Abdur Rahman, Jason Street, James Wooten +4
Quick and reliable measurement of wood chip moisture content is an everlasting problem for numerous forest-reliant industries such as biofuel, pulp and paper, and bio-refineries. M…
In Situ Soil Property Estimation for Autonomous Earthmoving Using Physics-Infused Neural Networks
W. Jacob Wagner, Ahmet Soylemezoglu, Dustin Nottage +1
A novel, learning-based method for in situ estimation of soil properties using a physics-infused neural network (PINN) is presented. The network is trained to produce estimates of…
A global unstructured, coupled, high-resolution hindcast of waves and storm surge
Lorenzo Mentaschi, Michalis Vousdoukas, Guillermo Garcia-Sanchez +7
Accurate information on waves and storm surges is essential to understand coastal hazards that are expected to increase in view of global warming and rising sea levels. Despite the…