4 ms·

My guess would be: not that much. But I work in the field of numerical mathematics and computational physics, so I could have some bias. :-D The more nuanced a

My guess would be: not that much. But I work in the field of numerical mathematics and computational physics, so I could have some bias. :-D 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.

ShamelessC 4y ago
Am an ML engineer with no experience on this subject. Do you think research like deep mind has done with now-casting could be useful here?
- mkbosmans 4y ago
  Yes, I think ML could be useful at places where the current physics modelling falls short. The nowcasting rain example from DeepMind is in some respects pretty comparable with the Next Ocean wave prediction. 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.

ShamelessC 4y ago

Am an ML engineer with no experience on this subject. Do you think research like deep mind has done with now-casting could be useful here?

mkbosmans 4y ago
Yes, I think ML could be useful at places where the current physics modelling falls short. The nowcasting rain example from DeepMind is in some respects pretty comparable with the Next Ocean wave prediction. 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.