Tootfinder

Output-recurrent gated state space model for multiphase flows modeling and uncertainty quantification of exhaust vehicles
Ruilin Chen, Ming Fang, Guihui Ma
https://arxiv.org/abs/2508.00588

Output-recurrent gated state space model for multiphase flows modeling and uncertainty quantification of exhaust vehicles
This paper presents an Output-Recurrent Gated State Space Model (OR-GSSM) for complex multiphase flows modeling and uncertainty quantification of exhaust vehicles during motion. By establishing the state-space formulation of the gas-liquid Navier-Stokes equations applying semigroup theory and Galerkin projection, explicitly characterizing the dynamic coupling evolution between the velocity, pressure, and volume fraction fields. A novel Gated State Space Transition (GSST) unit is designed to lea…

Physics-guided gated recurrent units for inversion-based feedforward control
Mingdao Lin, Max Bolderman, Mircea Lazar
https://arxiv.org/abs/2507.14052 http…

Physics-guided gated recurrent units for inversion-based feedforward control
Inversion-based feedforward control relies on an accurate model that describes the inverse system dynamics. The gated recurrent unit (GRU), which is a recent architecture in recurrent neural networks, is a strong candidate for obtaining such a model from data. However, due to their black-box nature, GRUs face challenges such as limited interpretability and vulnerability to overfitting. Recently, physics-guided neural networks (PGNNs) have been introduced, which integrate the prior physical mode…

Time-series forecasting for nonlinear high-dimensional system using hybrid method combining autoencoder and multi-parallelized quantum long short-term memory and gated recurrent unit
Makoto Takagi, Ryuji Kokubo, Misato Kurosawa, Tsubasa Ikami, Yasuhiro Egami, Hiroki Nagai, Takahiro Kashikawa, Koichi Kimura, Yutaka Takita, Yu Matsuda
https://

Time-series forecasting for nonlinear high-dimensional system using hybrid method combining autoencoder and multi-parallelized quantum long short-term memory and gated recurrent unit
A time-series forecasting method for high-dimensional spatial data is proposed. The method involves optimal selection of sparse sensor positions to efficiently represent the spatial domain, time-series forecasting at these positions, and estimation of the entire spatial distribution from the forecasted values via a learned decoder. Sensor positions are selected using a method based on combinatorial optimization. Introducing multi-parallelized quantum long short-term memory (MP-QLSTM) and gated …