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OceanData Consulting
Service 03 · AI

AI for oceanography

Add a learning component to your marine workflow when it answers an explicit validation protocol.

Your need

Observation–model blending to correct a field
FNO surrogate for temporal emulation of CROCO outputs
UNet for spatial downscaling when a training set exists

Required data

Reference simulations and initial states
Independent observations for evaluation
Simple baseline, metrics, splits and stopping criteria

Method

Separate training, validation and test sets
Compare with the physical model and a harmonic baseline
Document uncertainty, validity domain and inference cost

Deliverables

Validation protocol and report
Versioned inference code and configuration
Integration recommendation or a decision not to deploy
Documented example

The Brest current FNO reproduces a 250 m CROCO model; the published result is emulation from a model initial state, not a standalone forecast.

Limits and conditions
A surrogate depends on the quality and coverage of its training simulations.
Temporal FNO and spatial UNet address different problems.
No performance is promised before testing your data and baseline.
Process

A clear path from data to decision

01

Frame the need and decision criteria

02

Audit data and define the protocol

03

Run a reproducible analysis, model or training

04

Deliver, review and transfer

Quoted to scope: the effort depends on area, resolution, period and expected validation level.

Scope, available data and timeline are defined before any estimate.

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Let us discuss your coastal project

For any question or diagnostic request, write to us directly. Response within 48 hours.

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Describe your need, and we will get back to you quickly.

We respond within 48 business hours.