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Active Transfer Learning for Ocean Modeling (ATL)

Our main objective is to research ATL schemes and techniques to be applied to the modeling of ocean processes. The specific goals will be to:

  • Research, apply and implement learning theory and principles to ocean modeling
  • Develop idealized and realistic ocean modeling tests beds for ATL research
  • Incubate new (machine) learning schemes based on the idealized set-ups and test the best ones in realistic simulations.
  • Provide ocean data, simulations, software and methodology to the ATL project research teams
  • Collaborate and transfer of expertise, approaches, algorithms and software to and from naval laboratories and centers.
  • There are three types of ATL learning applications and scenarios that we plan to research using realistic ocean simulations and idealized simulations:

    • ATL directly from ocean measurements (either simulated or real): learning science/processes from data alone
    • ATL within realistic complex simulations (as complex as real-ocean)
      • Science challenge: find new processes in these complex simulations
      • Naval challenge: what are the keys for the Naval operator
    • ATL from mismatch between models and measurements