Learning Trajectories of Hamiltonian Systems with Neural Networks
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A4 Artikkeli konferenssijulkaisussa
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en
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12
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Artificial Neural Networks and Machine Learning – ICANN 2022, pp. 562–573, Lecture Notes in Computer Science ; Volume 13529
Abstract
Modeling of conservative systems with neural networks is an area of active research. A popular approach is to use Hamiltonian neural networks (HNNs) which rely on the assumptions that a conservative system is described with Hamilton's equations of motion. Many recent works focus on improving the integration schemes used when training HNNs. In this work, we propose to enhance HNNs with an estimation of a continuous-time trajectory of the modeled system using an additional neural network, called a deep hidden physics model in the literature. We demonstrate that the proposed integration scheme works well for HNNs, especially with low sampling rates, noisy and irregular observations.Description
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Haitsiukevich, K & Ilin, A 2022, Learning Trajectories of Hamiltonian Systems with Neural Networks. in E Pimenidis, M Aydin, P Angelov, C Jayne & A Papaleonidas (eds), Artificial Neural Networks and Machine Learning – ICANN 2022. Lecture Notes in Computer Science, vol. 13529, Springer, Bristol, UK, pp. 562–573, International Conference on Artificial Neural Networks, Bristol, United Kingdom, 06/09/2022. https://doi.org/10.1007/978-3-031-15919-0_47