… physics-informed echo state network (ESN) to predict the evolution of chaotic systems.
Compared to conventional ESNs, the physics-informed … , the physics-informed ESNs improve the …

… a physics-informed Echo State Network (ESN) to predict the evolution of chaotic systems.
Compared to conventional ESNs, the physics-informed … , where the physics-informed ESNs …

… We extend the Physics-Informed Echo State Network (PI-ESN) framework to reconstruct the
evolution of an unmeasured state (hidden state) in a chaotic system. The PI-ESN is trained …

… In this work, we have proposed an extension of Physics-Informed Echo State Networks (PI-ESN)
that make them work with external inputs. This augmentation allows PI-ESNs to be …

… data, while the deep echo state network with multiple adaptive … Physics-informed machine
learning has emerged as a … In summary, physics-informed machine learning can seamlessly …

… extending the network’s ability to reconstruct hidden states in chaotic systems. In Sect. 2, we
… the proposed network: the Automatic-differentiated Physics-Informed Echo State Network (…

… Echo State Networks (ESNs) are recurrent neural networks … training of ESNs, Physics-Informed
Echo State Networks (PI-… regression term and the physics-informed loss term in the total …

… Actually, it is noted as the most particular idea in this study of physics-informed ESN, to
take care of such a consistency in the sequential readouts regarding the general principle of …

… , physics-informed echo state network (ESN) is discussed on the attempt to train a reservoir
model absolutely in physics-informed … Aiming at the fully physics-informed reservoir model, a …

… method, the continuoustime echo state network (CTESN), for … Physics-Informed Neural
Networks (PINNs), Long Short Term Memory (LSTM) networks, and discrete echo state networks (…