Physics-informed echo state networks for chaotic systems forecasting
We propose a physics-informed Echo State Network (ESN) to predict the evolution of chaotic
systems. Compared to conventional ESNs, the physics-informed ESNs are trained to solve
supervised learning tasks while ensuring that their predictions do not violate physical laws.
This is achieved by introducing an additional loss function during the training of the ESNs,
which penalizes non-physical predictions without the need of any additional training data.
This approach is demonstrated on a chaotic Lorenz system, where the physics-informed …
systems. Compared to conventional ESNs, the physics-informed ESNs are trained to solve
supervised learning tasks while ensuring that their predictions do not violate physical laws.
This is achieved by introducing an additional loss function during the training of the ESNs,
which penalizes non-physical predictions without the need of any additional training data.
This approach is demonstrated on a chaotic Lorenz system, where the physics-informed …
Physics-Informed Echo State Networks for Chaotic Systems Forecasting
We propose a physics-informed Echo State Network (ESN) to predict the evolution of chaotic
systems. Compared to conventional ESNs, the physics-informed ESNs are trained to solve
supervised learning tasks while ensuring that their predictions do not violate physical laws.
This is achieved by introducing an additional loss function during the training of the ESNs,
which penalizes non-physical predictions without the need of any additional training data.
This approach is demonstrated on a chaotic Lorenz system, where the physics-informed …
systems. Compared to conventional ESNs, the physics-informed ESNs are trained to solve
supervised learning tasks while ensuring that their predictions do not violate physical laws.
This is achieved by introducing an additional loss function during the training of the ESNs,
which penalizes non-physical predictions without the need of any additional training data.
This approach is demonstrated on a chaotic Lorenz system, where the physics-informed …
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