Using Neural Implicit Flow To Represent Latent Dynamics Of Canonical Systems
Fuente:
arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866910425010929664 |
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| author | Nasim, Imran Almeida, Joaõ Lucas de Sousa |
| author_facet | Nasim, Imran Almeida, Joaõ Lucas de Sousa |
| contents | The recently introduced class of architectures known as Neural Operators has emerged as highly versatile tools applicable to a wide range of tasks in the field of Scientific Machine Learning (SciML), including data representation and forecasting. In this study, we investigate the capabilities of Neural Implicit Flow (NIF), a recently developed mesh-agnostic neural operator, for representing the latent dynamics of canonical systems such as the Kuramoto-Sivashinsky (KS), forced Korteweg-de Vries (fKdV), and Sine-Gordon (SG) equations, as well as for extracting dynamically relevant information from them. Finally we assess the applicability of NIF as a dimensionality reduction algorithm and conduct a comparative analysis with another widely recognized family of neural operators, known as Deep Operator Networks (DeepONets). |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_17535 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Using Neural Implicit Flow To Represent Latent Dynamics Of Canonical Systems Nasim, Imran Almeida, Joaõ Lucas de Sousa Machine Learning Artificial Intelligence The recently introduced class of architectures known as Neural Operators has emerged as highly versatile tools applicable to a wide range of tasks in the field of Scientific Machine Learning (SciML), including data representation and forecasting. In this study, we investigate the capabilities of Neural Implicit Flow (NIF), a recently developed mesh-agnostic neural operator, for representing the latent dynamics of canonical systems such as the Kuramoto-Sivashinsky (KS), forced Korteweg-de Vries (fKdV), and Sine-Gordon (SG) equations, as well as for extracting dynamically relevant information from them. Finally we assess the applicability of NIF as a dimensionality reduction algorithm and conduct a comparative analysis with another widely recognized family of neural operators, known as Deep Operator Networks (DeepONets). |
| title | Using Neural Implicit Flow To Represent Latent Dynamics Of Canonical Systems |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2404.17535 |