Felix Köster
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View article: Attention-Enhanced Reservoir Computing as a Multiple Dynamical System Approximator
Attention-Enhanced Reservoir Computing as a Multiple Dynamical System Approximator Open
Reservoir computing has proven effective for tasks such as time-series prediction, particularly in the context of chaotic systems. However, conventional reservoir computing frameworks often face challenges in achieving high prediction accu…
View article: Data-driven acceleration of multi-physics simulations
Data-driven acceleration of multi-physics simulations Open
Multi-physics simulations play a crucial role in understanding complex systems. However, their computational demands are often prohibitive due to high dimensionality and complex interactions, such that actual calculations often rely on app…
View article: Data-Driven Acceleration of Multi-Physics Simulations
Data-Driven Acceleration of Multi-Physics Simulations Open
Multi-physics simulations play a crucial role in understanding complex systems. However, their computational demands are often prohibitive due to high dimensionality and complex interactions, such that actual calculations often rely on app…
View article: Attention-Enhanced Reservoir Computing
Attention-Enhanced Reservoir Computing Open
Photonic reservoir computing has been successfully utilized in time-series prediction as the need for hardware implementations has increased. Prediction of chaotic time series remains a significant challenge, an area where the conventional…
View article: Data-informed reservoir computing for efficient time-series prediction
Data-informed reservoir computing for efficient time-series prediction Open
We propose a new approach to dynamical system forecasting called data-informed-reservoir computing (DI-RC) that, while solely being based on data, yields increased accuracy, reduced computational cost, and mitigates tedious hyper-parameter…
View article: Data-driven forecasting of nonequilibrium solid-state dynamics
Data-driven forecasting of nonequilibrium solid-state dynamics Open
We present a data-driven approach to efficiently approximate nonlinear\ntransient dynamics in solid-state systems. Our proposed machine-learning model\ncombines a dimensionality reduction stage with a nonlinear vector\nautoregression schem…
View article: Master Memory Function for Delay-Based Reservoir Computers With Single-Variable Dynamics
Master Memory Function for Delay-Based Reservoir Computers With Single-Variable Dynamics Open
We show that many delay-based reservoir computers considered in the literature can be characterized by a universal master memory function (MMF). Once computed for two independent parameters, this function provides linear memory capacity fo…
View article: Deriving task specific performance from the information processing capacity of a reservoir computer
Deriving task specific performance from the information processing capacity of a reservoir computer Open
In the reservoir computing literature, the information processing capacity is frequently used to characterize the computing capabilities of a reservoir. However, it remains unclear how the information processing capacity connects to the pe…
View article: Role of delay-times in delay-based photonic reservoir computing [Invited]
Role of delay-times in delay-based photonic reservoir computing [Invited] Open
Delay-based reservoir computing has gained a lot of attention due to the relative simplicity with which this concept can be implemented in hardware. However, unnecessary constraints are commonly placed on the relationship between the delay…
View article: The role of delay-times in delay-based Photonic Reservoir Computing
The role of delay-times in delay-based Photonic Reservoir Computing Open
Delay-based reservoir computing has gained a lot of attention due to the relative simplicity with which this concept can be implemented in hardware. However,there is still an misconception about the relationship between the delay-time and …
View article: Master memory function for delay-based reservoir computers with\n single-variable dynamics
Master memory function for delay-based reservoir computers with\n single-variable dynamics Open
We show that many delay-based reservoir computers considered in the\nliterature can be characterized by a universal master memory function (MMF).\n Once computed for two independent parameters, this function provides linear\nmemory capacit…
View article: Collective Coherence Resonance in Networks of Optical Neurons
Collective Coherence Resonance in Networks of Optical Neurons Open
The dynamical properties of an optical neuron formed by a quantum dot semiconductor laser model subjected to optical injection and optical feedback are analyzed. The parameter space spanned by the injection strength and frequency detuning …
View article: Insight into delay based reservoir computing via eigenvalue analysis
Insight into delay based reservoir computing via eigenvalue analysis Open
In this paper we give a profound insight into the computation capability of delay based reservoir computing via an eigenvalue analysis. We concentrate on the task-independent memory capacity to quantify the reservoir performance and compar…
View article: Limitations of the recall capabilities in delay based reservoir computing systems
Limitations of the recall capabilities in delay based reservoir computing systems Open
We analyze the memory capacity of a delay based reservoir computer with a Hopf normal form as nonlinearity and numerically compute the linear as well as the higher order recall capabilities. A possible physical realisation could be a laser…
View article: Improving Delay Based Reservoir Computing via Eigenvalue Analysis.
Improving Delay Based Reservoir Computing via Eigenvalue Analysis. Open
We analyze the reservoir computation capability of the Lang-Kobayashi system by comparing the numerically computed recall capabilities and the eigenvalue spectrum. We show that these two quantities are deeply connected, and thus the reserv…
View article: Deep time-delay reservoir computing: Dynamics and memory capacity
Deep time-delay reservoir computing: Dynamics and memory capacity Open
The deep time-delay reservoir computing concept utilizes unidirectionally connected systems with time-delays for supervised learning. We present how the dynamical properties of a deep Ikeda-based reservoir are related to its memory capacit…