Search Results for author:
Found 7 papers, 0 papers with code
On the Effect of Quantization on Dynamic Mode Decomposition
Dynamic Mode Decomposition (DMD) is a widely used data-driven algorithm for estimating the Koopman Operator. This paper investigates how the estimation process is affected when the data is quantized.
Feature-Based Echo-State Networks: A Step Towards Interpretability and Minimalism in Reservoir Computer
This paper proposes a novel and interpretable recurrent neural-network structure using the echo-state network (ESN) paradigm for time-series prediction.
Temporally-Consistent Koopman Autoencoders for Forecasting Dynamical Systems
Absence of sufficiently high-quality data often poses a key challenge in data-driven modeling of high-dimensional spatio-temporal dynamical systems.
Sequential Learning from Noisy Data: Data-Assimilation Meets Echo-State Network
This paper explores the problem of training a recurrent neural network from noisy data.
Delay Embedded Echo-State Network: A Predictor for Partially Observed Systems
This paper considers the problem of data-driven prediction of partially observed systems using a recurrent neural network.
Investigation of A Collective Decision Making System of Different Neighbourhood-Size Based on Hyper-Geometric Distribution
The study of collective decision making system has become the central part of the Swarm- Intelligence Related research in recent years.
Multi-Agent Shape Formation and Tracking Inspired from a Social Foraging Dynamics
Principle of Swarm Intelligence has recently found widespread application in formation control and automated tracking by the automated multi-agent system.
