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Found 5 papers, 2 papers with code
Out-of-Domain Generalization in Dynamical Systems Reconstruction
We explain why and how out-of-domain (OOD) generalization (OODG) in DSR profoundly differs from OODG considered elsewhere in machine learning.
Generalized Teacher Forcing for Learning Chaotic Dynamics
Here we report that a surprisingly simple modification of teacher forcing leads to provably strictly all-time bounded gradients in training on chaotic systems, and, when paired with a simple architectural rearrangement of a tractable RNN design, piecewise-linear RNNs (PLRNNs), allows for faithful reconstruction in spaces of at most the dimensionality of the observed system.
Integrating Multimodal Data for Joint Generative Modeling of Complex Dynamics
Many, if not most, systems of interest in science are naturally described as nonlinear dynamical systems.
Tractable Dendritic RNNs for Reconstructing Nonlinear Dynamical Systems
In many scientific disciplines, we are interested in inferring the nonlinear dynamical system underlying a set of observed time series, a challenging task in the face of chaotic behavior and noise.
Tractable Dendritic RNNs for Identifying Unknown Nonlinear Dynamical Systems
In many scientific disciplines, we are interested in inferring the nonlinear dynamical system underlying a set of observed time series, a challenging task in the face of chaotic behavior and noise.
