There are also open source tide and wave models, which is what researchers use and are fairly accurate. Not sure how much this commercial product differs, but I know the Navy etc. use the open source ones with in house adjustments.
In contrast, the Next Ocean product is meant to model the direct area around a single ship, predicting minutes in advance with a second by second granularity. They are able to reuse the raw data coming from the navigation radar that is already installed on every sea-going ship. It uses the backscatter from water ripples to determine the wave field around the ship. Interestingly, for navigational purposes exactly this part of the data is considered noise and filtered out by the on board navigation system. (https://nextocean.nl/technology.php)
[I did some work on the software implementation for Next Ocean a couple of years back]
Anyway, neither type of model has anything to do with rogue waves.
The more nuanced answer would be that taking the raw radar data as input to e.g. a neural network and train that to output the predicted timeseries of future ship motion is not feasible. It would take a giant network and too much compute to train for very unreliable results.
This problem consists of a lot of subproblems, most of which are pretty well understood. For example how to translate the 6-dof motion of a ship to the vertical displacement of a heli platform on that ship is just some simple coordinate transforms. You don't gain anything by including that in the neural net. Potentially some data science techniques could be useful to handle some of the less understood submodels. Sort of like it is done in CFD with NN as a turbulence model within an existing PDE solver.
In the wave prediction case, wave propagation and dispersion is pretty well understood. But one could add ML-based nonlinear terms to the equations to capture everything we don't know. That has the possibility of giving better predictions.
In contrast with the rogue wave example, there is a lot of relevant input data (the radar backscatter) and the model output can be verified after the fact (the ship's 6-dof acceleration). What I was objecting to was the idea of: slap a ML model on the whole problem and call it a day.
A more advanced example of a technique that was used before it was called data science is data assimilation (DA)[1]. Here you assume that you have observations (e.g. sensor data) that you want to use to inform the model, but they are noisy in some sense. With DA you take a set of observations at t=0 and fit a numerical model to that. Then you time-step the model to t=1 where you have new observations. The model and observations don't necessarily agree, but there is value in incorporating information from both. Based on e.g. your statistical description of the sensor noise, DA techniques give you the tools to combine data and models.
A good example of DA is 4DCOOL[2], combining temperature sensors in a datacenter with a CFD model. Because the model is physics-based, after some time you get a good idea of the temperature distribution in the whole room, even if you only have pretty sparse sensor data. (disclosure: I work for the company)
[1] https://en.wikipedia.org/wiki/Data_assimilation [2] https://www.vortech.nl/en/projects/a-digital-twin-for-the-in...
> one moment, just downloading this project off github
> just downloading few gigs of dependencies
“Cannot use import statement outside module”