Environmental conditions such as wave height and wave direction are often incomplete, unreliable, or unavailable in live operations.
Physics-based models and machine learning algorithms infer environmental forces from measured vessel motions.
Continuous estimation of environmental loading and directionality using operational sensor data.
Structural response involves complex interactions between loading, dynamics, and material properties that defy simplified analysis.
Coupled models estimate stress, displacement, and fatigue accumulation in real time — calibrated against measured field data.
Ongoing visibility into structural performance and degradation, supporting engineering assessment and operational decision-making.
Critical parameters such as displacement and alignment often cannot be measured directly due to limited access, sensor survivability, or deployment cost.
Patented computer vision and physics-based inference extract quantitative measurements where traditional sensors cannot be deployed.
High-precision estimation of displacement, alignment, and multi-degree-of-freedom motion in environments where traditional sensing cannot be deployed or maintained.
High-fidelity engineering models are often too computationally intensive and too disconnected from live data for real-time deployment.
Reduced-order models and machine learning representations convert complex simulations into deployable systems that operate on live data.
A transition from analysis-driven models to operational engineering capability — where models function as embedded components of real-world systems rather than isolated analytical tools.