Tensor-Train Weak SINDy: Identifying High-Dimensional Nonlinear Dynamics
Weak Sparse Identification of Nonlinear Dynamics (WSINDy) provides a noise-robust approach for learning dynamical systems from data without requiring numerical differentiation. However, for high-dimensional systems, tensor-product libraries of candidate functions grow exponentially with the state dimension, making standard WSINDy expensive in both computation and memory. The Multidimensional Approximation of Nonlinear Dynamics (MANDy) addresses this scaling through a tensor-train (TT) representation of the candidate library, but does not provide a mechanism for sparse model selection. Here, we combine these approaches to develop TT-WSINDy, which performs the weak-form transformation, regr...
| Authors | Will Houser, Vanja Dukic, David M. Bortz |
|---|---|
| Primary category | cs.LG |
| Published | 8 September 2026 |
| Last updated at source | 1 October 2026 |
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