The scaling of physics-informed machine learning with data and dimensions
We quantify how incorporating physics into neural network design can significantly improve the learning and forecasting of dynamical systems, even nonlinear systems of many dimensions. We train conventional and Hamiltonian neural networks on increasingly difficult dynamical systems and compute their...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2020-03-01
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| Series: | Chaos, Solitons & Fractals: X |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590054420300270 |