SYSTEM AND METHOD FOR IMPLEMENTING A DATA-DRIVEN FRAMEWORK FOR OBSERVATION, DATA ASSIMILATION, AND PREDICTION OF OCEAN CURRENTS
A framework is provided where a stochastic fully data-driven model (FDDM) predicts states of the ocean for both short and long-time scales with uncertainty quantification. The FDDM, which can generate a large number of ensembles at low computational cost, is integrated with a multi-layer perceptron-based data assimilation algorithm, which can efficiently and accurately assimilate Lagrangian ocean observations.
1 . A system comprising:
at least one processor; and,
at least one memory having instructions stored thereon that, when executed by the at least one processor, cause the processor at least to:
generate data ensembles using a stochastic data driven prediction model trained on ocean simulations;
receive observation data;
estimate an analysis state using a multi-layer perceptron that represents the analysis state as a non-linear conjunction of observations and minimizing variance across the ensembles; and
selectively output a prediction on ocean conditions.
2 . The system as set forth in claim 1 , wherein the analysis state is used as an initial condition to perform forecasting or generate the ensembles for a next cycle.
3 . The system as set forth in claim 1 , wherein the stochastic data drive prediction model comprises a fully data driven model which is a stochastic variational model or a variational autoencoder configured to predict a large number of ensembles of states of the ocean.
4 . The system as set forth in claim 3 , wherein the fully data driven model comprises a 4th order Runge Kutta (RK4) based time-integrator for accurate estimation of future time steps with low error growths.
5 . The system as set forth in claim 3 , wherein the fully data driven model is configured to add physical constraints within its architecture or through regularization.
6 . The system as set forth in claim 3 , wherein the fully data driven model is configured to perform robust uncertainty quantification through the statistics obtained from the large ensembles.
7 . The system as set forth in claim 1 , wherein the multi-layer perceptron is configured to perform a Lagrangian data assimilation method capable of integrating distributed sensor observations from the ocean.
8 . The system as set forth in claim 7 , wherein the multi-layer perceptron is non-linear and free of ad-hoc choices in de-correlation lengths.
9 . The system as set forth in claim 7 , wherein the multi-layer perceptron is implemented on the same device as the fully data driven model allowing for on-the-fly data assimilation.
10 . A method comprising:
generating data ensembles using a stochastic data driven prediction model trained on ocean simulations;
receiving observation data;
estimating an analysis state using a multi-layer perceptron that represents the analysis state as a non-linear conjunction of observations and minimizing variance across the ensembles; and
selectively outputting a prediction on ocean conditions.
11 . The method as set forth in claim 10 , wherein the analysis state is used as an initial condition to perform forecasting or generate the ensembles for a next cycle.
12 . The method as set forth in claim 10 , wherein the stochastic data drive prediction model comprises a fully data driven model which is a stochastic variational model or a variational autoencoder configured to predict a large number of ensembles of states of the ocean at low computational cost.
13 . The method as set forth in claim 10 , wherein the fully data driven model comprises a 4th order Runge Kutta (RK4) based time-integrator for accurate estimation of future time steps with low error growths.
14 . The system as set forth in claim 11 , wherein the fully data driven model is configured to add physical constraints within its architecture or through regularization.
15 . The system as set forth in claim 11 , wherein the fully data driven model is configured to perform robust uncertainty quantification through the statistics obtained from the large ensembles.
16 . The system as set forth in claim 10 , wherein the multi-layer perceptron is configured to perform a Lagrangian data assimilation method capable of integrating distributed sensor observations from the ocean.
17 . The system as set forth in claim 16 , wherein the multi-layer perceptron is non-linear and free of ad-hoc choices in de-correlation lengths.
18 . The system as set forth in claim 16 , wherein the multi-layer perceptron is implemented on the same device as the fully data driven model allowing for on-the-fly data assimilation.
19 . A non-transitory computer readable medium having stored thereon instructions that, when executed by a processor, cause a system to:
generate data ensembles using a stochastic data driven prediction model trained on ocean simulations;
receive observation data;
estimate an analysis state using a multi-layer perceptron that represents the analysis state as a non-linear conjunction of observations and minimizing variance across the ensembles; and
selectively output a prediction on ocean conditions.
20 . The non-transitory computer readable medium as set forth in claim 19 , wherein the analysis state is used as an initial condition to perform forecasting or generate the ensembles for a next cycle.