Neural Integral Equations
Fuente:
arXiv
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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2022
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| Soggetti: | |
| Accesso online: | |
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| _version_ | 1866913496178884608 |
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| author | Zappala, Emanuele Fonseca, Antonio Henrique de Oliveira Caro, Josue Ortega Moberly, Andrew Henry Higley, Michael James Cardin, Jessica van Dijk, David |
| author_facet | Zappala, Emanuele Fonseca, Antonio Henrique de Oliveira Caro, Josue Ortega Moberly, Andrew Henry Higley, Michael James Cardin, Jessica van Dijk, David |
| contents | Nonlinear operators with long distance spatiotemporal dependencies are fundamental in modeling complex systems across sciences, yet learning these nonlocal operators remains challenging in machine learning. Integral equations (IEs), which model such nonlocal systems, have wide ranging applications in physics, chemistry, biology, and engineering. We introduce Neural Integral Equations (NIE), a method for learning unknown integral operators from data using an IE solver. To improve scalability and model capacity, we also present Attentional Neural Integral Equations (ANIE), which replaces the integral with self-attention. Both models are grounded in the theory of second kind integral equations, where the indeterminate appears both inside and outside the integral operator. We provide theoretical analysis showing how self-attention can approximate integral operators under mild regularity assumptions, further deepening previously reported connections between transformers and integration, and deriving corresponding approximation results for integral operators. Through numerical benchmarks on synthetic and real world data, including Lotka-Volterra, Navier-Stokes, and Burgers' equations, as well as brain dynamics and integral equations, we showcase the models' capabilities and their ability to derive interpretable dynamics embeddings. Our experiments demonstrate that ANIE outperforms existing methods, especially for longer time intervals and higher dimensional problems. Our work addresses a critical gap in machine learning for nonlocal operators and offers a powerful tool for studying unknown complex systems with long range dependencies. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2209_15190 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Neural Integral Equations Zappala, Emanuele Fonseca, Antonio Henrique de Oliveira Caro, Josue Ortega Moberly, Andrew Henry Higley, Michael James Cardin, Jessica van Dijk, David Machine Learning Numerical Analysis Dynamical Systems Computational Physics Nonlinear operators with long distance spatiotemporal dependencies are fundamental in modeling complex systems across sciences, yet learning these nonlocal operators remains challenging in machine learning. Integral equations (IEs), which model such nonlocal systems, have wide ranging applications in physics, chemistry, biology, and engineering. We introduce Neural Integral Equations (NIE), a method for learning unknown integral operators from data using an IE solver. To improve scalability and model capacity, we also present Attentional Neural Integral Equations (ANIE), which replaces the integral with self-attention. Both models are grounded in the theory of second kind integral equations, where the indeterminate appears both inside and outside the integral operator. We provide theoretical analysis showing how self-attention can approximate integral operators under mild regularity assumptions, further deepening previously reported connections between transformers and integration, and deriving corresponding approximation results for integral operators. Through numerical benchmarks on synthetic and real world data, including Lotka-Volterra, Navier-Stokes, and Burgers' equations, as well as brain dynamics and integral equations, we showcase the models' capabilities and their ability to derive interpretable dynamics embeddings. Our experiments demonstrate that ANIE outperforms existing methods, especially for longer time intervals and higher dimensional problems. Our work addresses a critical gap in machine learning for nonlocal operators and offers a powerful tool for studying unknown complex systems with long range dependencies. |
| title | Neural Integral Equations |
| topic | Machine Learning Numerical Analysis Dynamical Systems Computational Physics |
| url | https://arxiv.org/abs/2209.15190 |