A new preprint on arXiv examines how machine learning can be used to identify and model metastable behavior in physical systems. Metastability describes systems that linger in quasi-stable states and then shift abruptly under rare perturbations, a pattern seen across many areas of physics. The work targets the challenge of detecting and representing these transitions computationally.
A revised arXiv preprint introduces fSRD, a method that builds Koopman operator representations for highly nonlinear chaotic dynamical systems without manual operator selection. The approach decomposes spectral regions with fuzzy boundaries and adapts its architecture during training to balance complexity, expressiveness, and data efficiency. It targets the difficulty of modeling chaotic dynamics where conventional machine learning trades off accuracy against data needs.
A new arXiv preprint introduces TT-weak-SINDy, a method that combines weak-form system identification with tensor-train decompositions. The approach is designed to reduce the memory and computational burden that current weak-form techniques face when applied to high-dimensional dynamical systems. The authors position it as a scalable alternative for data-driven discovery of nonlinear dynamics.