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Found 7 papers, 0 papers with code
Flow Map Learning for Unknown Dynamical Systems: Overview, Implementation, and Benchmarks
Flow map learning (FML), in conjunction with deep neural networks (DNNs), has shown promises for data driven modeling of unknown dynamical systems.
Sub-aperture SAR Imaging with Uncertainty Quantification
As proposed, the method was not well-suited for large problems, however, as the sampling was inefficient.
Learning Fine Scale Dynamics from Coarse Observations via Inner Recurrence
Recent work has focused on data-driven learning of the evolution of unknown systems via deep neural networks (DNNs), with the goal of conducting long term prediction of the dynamics of the unknown system.
Deep Learning of Chaotic Systems from Partially-Observed Data
A distinct feature of chaotic systems is that even the smallest perturbations will lead to large (albeit bounded) deviations in the solution trajectories.
Robust Modeling of Unknown Dynamical Systems via Ensemble Averaged Learning
In the proposed ensemble averaging method, multiple models are independently trained and model predictions are averaged at each time step.
Deep Neural Network Modeling of Unknown Partial Differential Equations in Nodal Space
Consequently, a trained DNN defines a predictive model for the underlying unknown PDE over structureless grids.
Estimation and uncertainty quantification for piecewise smooth signal recovery
This expands the class of problems available to Bayesian learning to include, e. g., inverse problems dealing with the recovery of piecewise smooth functions or signals from data.
