Linear Reservoir
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Related Articles from SNS
Fast Linear Reservoirs via Diagonalization
arXiv:2602.19802v3 Announce Type: replace Abstract: We introduce a diagonalization-based optimization for Linear Echo State Networks (ESNs) that reduces the per-step computational complexity of reservoir state updates from quadratic to linear. By reformulating reservoir dynamics in the eigenbasis of the recurrent matrix, the recurrent update becomes a set of independent element-wise operations, eliminating the matrix multiplication. We further propose three methods to use our optimization...
Linear Reservoir: A Diagonalization-Based Optimization
arXiv:2602.19802v2 Announce Type: replace Abstract: We introduce a diagonalization-based optimization for Linear Echo State Networks (ESNs) that reduces the per-step computational complexity of reservoir state updates from quadratic to linear. By reformulating reservoir dynamics in the eigenbasis of the recurrent matrix, the recurrent update becomes a set of independent element-wise operations, eliminating the matrix multiplication. We further propose three methods to use our optimization...
Residual Reservoir Memory Networks
Announce Type: replace Abstract: We introduce a novel class of untrained Recurrent Neural Networks (RNNs) within the Reservoir Computing (RC) paradigm, called Residual Reservoir Memory Networks (ResRMNs). ResRMN combines a linear memory reservoir with a non-linear reservoir, where the latter is based on residual orthogonal connections along the temporal dimension for enhanced long-term propagation of the input. The resulting reservoir state dynamics are studied through the lens of linear...
Polalrized reservoirs in dynamics of polariton condensation
arXiv:2606.04808v1 Announce Type: new Abstract: We review the problem of description of the dynamics of driven-disspipative spinor polariton condensates, focusing on the terms corresponding to the coupling between a macroscopic wavefunction of the condensdate and incoherent excitonic reservoir created by a non-resonant pump. We demonstrate that the existing version of the theory breaks down in case, when reservoir has non-zero components of the Stokes vector corresponding to in-plane linear...
ParalESN: Enabling parallel information processing in Reservoir Computing
arXiv:2601.22296v2 Announce Type: replace Abstract: Reservoir Computing (RC) has established itself as an efficient paradigm for temporal processing. However, its scalability remains severely constrained by the need to process temporal data sequentially and the prohibitive memory footprint of high-dimensional reservoirs. To address these limitations, we revisit RC through the lens of structured operators and state space modeling, introducing Parallel Echo State Network (ParalESN).
Electricity price forecasting across Norway's five bidding zones in the post-crisis era
Announce Type: replace Abstract: Norway's electricity market is heavily dominated by hydropower, but the 2021-2022 energy crisis and stronger integration with Continental Europe have fundamentally altered price formation, reducing the reliability of forecasting models calibrated on historical data. Despite the critical need for updated models, a unified benchmark evaluating feature contributions across all structurally diverse Norwegian bidding zones remains lacking. Here we present a...
Short-Term Synaptic Plasticity Stabilizes Goal-Conditioned Dynamics in a PFC-Inspired Reservoir Model for Multistep Goal-Directed Action Planning
arXiv:2606.03481v1 Announce Type: cross Abstract: The prefrontal cortex (PFC) maintains goal information for action planning, but how recurrent circuits preserve it in an action-usable form over behavioral timescales remains unclear. Here we ask whether short-term synaptic plasticity (STP) can stabilize goal information as action-usable, goal-conditioned dynamics. We incorporated STP into a PFC-inspired reservoir computing model with basal-ganglia-inspired temporal-difference readout...
A Machine Learning-Enhanced Hopf-Cole Formulation for Nonlinear Gas Flow in Porous Media
Announce Type: replace Abstract: Accurate modeling of gas flow through porous media is critical for many technological applications, including reservoir performance prediction, carbon capture and sequestration, and fuel cells and batteries. However, such modeling remains challenging due to strong nonlinear behavior and uncertainty in model parameters. In particular, gas slippage effects described by the Klinkenberg model introduce pressure-dependent permeability, which complicates numerical...
A Machine Learning-Enhanced Hopf-Cole Formulation for Nonlinear Gas Flow in Porous Media
Announce Type: replace-cross Abstract: Accurate modeling of gas flow through porous media is critical for many technological applications, including reservoir performance prediction, carbon capture and sequestration, and fuel cells and batteries. However, such modeling remains challenging due to strong nonlinear behavior and uncertainty in model parameters. In particular, gas slippage effects described by the Klinkenberg model introduce pressure-dependent permeability, which complicates...
Light-induced quantum friction of carbon nanotubes in water
Abstract Friction slows down moving objects at both macroscopic and microscopic scales1. At the electronic level, quantum friction describes direct transfer of momentum between a liquid and the electrons of a solid2. Owing to its microscopic nature, this phenomenon remains experimentally challenging to capture3.