13 papers
Robust data-driven discovery of fractional differential equations via weak formulations and Pareto-based subset selection
Pongpisit Thanasutives, Yoshinobu Kawahara
Fractional partial differential equations describe nonlocal dynamics, but discovering them from noisy data is difficult because fractional differentiation amplifies high-frequency…
Dynamics-aware identification of governing equations from sparse and noisy data
Pongpisit Thanasutives, Yoshinobu Kawahara
Sparse identification of nonlinear dynamics (SINDy) and PDE functional identification (PDE-FIND) recover parsimonious ordinary and partial differential equations (ODEs and PDEs) fr…
Timesteps of Mamba Align with Human Reading Times
Yuji Yamamoto, Shinnosuke Isono, Yoshinobu Kawahara +1
This study demonstrates an alignment of per-word processing time in a popular state-space language model Mamba and human readers. In Mamba, the recurrent state transition at each l…
Structured Noise Adaptation for Sequential Bayesian Filtering with Embedded Latent Transfer Operators
Naichang Ke, Pongpisit Thanasutives, Yoshinobu Kawahara
Kalman filters based on the Embedded Latent Transfer Operators (ELTO) emerge as novel statistical tools for sequential state estimation. However, a critical limitation stems from t…
Deep Spectral Learning of Embedded Latent Transfer Operators for Stochastic Dynamical Systems
Ryogo Tanaka, Yoshinobu Kawahara
We propose a spectral learning method for stochastic nonlinear dynamical systems represented with embedded latent transfer operators in deep feature spaces. We instantiate the meth…
Mesh Field Theory: Port-Hamiltonian Formulation of Mesh-Based Physics
Satoshi Noguchi, Yoshinobu Kawahara
We present Mesh Field Theory (MeshFT) and its neural realization, MeshFT-Net: a structure-preserving framework for mesh-based continuum physics that cleanly separates the physics'…