Newton versus the machine: solving the chaotic three-body problem using deep neural networks
Since its formulation by Sir Isaac Newton, the problem of solving the equations of motion for three bodies under their own gravitational force has remained practically unsolved. Currently, the solution for a given initialization can only be found by performing laborious iterative calculations that h...
| Những tác giả chính: | , , , |
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| Định dạng: | Journal article |
| Ngôn ngữ: | English |
| Được phát hành: |
Oxford University Press
2020
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| _version_ | 1826305818889289728 |
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| author | Breen, PG Foley, CN Boekholt, T Zwart, SP |
| author_facet | Breen, PG Foley, CN Boekholt, T Zwart, SP |
| author_sort | Breen, PG |
| collection | OXFORD |
| description | Since its formulation by Sir Isaac Newton, the problem of solving the equations of motion for three bodies under their own gravitational force has remained practically unsolved. Currently, the solution for a given initialization can only be found by performing laborious iterative calculations that have unpredictable and potentially infinite computational cost, due to the system's chaotic nature. We show that an ensemble of converged solutions for the planar chaotic three-body problem obtained using an arbitrarily precise numerical integrator can be used to train a deep artificial neural network (ANN) that, over a bounded time interval, provides accurate solutions at a fixed computational cost and up to 100 million times faster than the numerical integrator. In addition, we demonstrate the importance of training an ANN using converged solutions from an arbitrary precise integrator, relative to solutions computed by a conventional fixed precision integrator, which can introduce errors in the training data, due to numerical round-off and time discretization, that are learned by the ANN. Our results provide evidence that, for computationally challenging regions of phase space, a trained ANN can replace existing numerical solvers, enabling fast and scalable simulations of many-body systems to shed light on outstanding phenomena such as the formation of black hole binary systems or the origin of the core collapse in dense star clusters. |
| first_indexed | 2024-03-07T06:38:37Z |
| format | Journal article |
| id | oxford-uuid:f8845cc4-44b3-4abf-861c-947eeafd6c1a |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-07T06:38:37Z |
| publishDate | 2020 |
| publisher | Oxford University Press |
| record_format | dspace |
| spelling | oxford-uuid:f8845cc4-44b3-4abf-861c-947eeafd6c1a2022-03-27T12:50:50ZNewton versus the machine: solving the chaotic three-body problem using deep neural networksJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:f8845cc4-44b3-4abf-861c-947eeafd6c1aEnglishSymplectic ElementsOxford University Press2020Breen, PGFoley, CNBoekholt, TZwart, SPSince its formulation by Sir Isaac Newton, the problem of solving the equations of motion for three bodies under their own gravitational force has remained practically unsolved. Currently, the solution for a given initialization can only be found by performing laborious iterative calculations that have unpredictable and potentially infinite computational cost, due to the system's chaotic nature. We show that an ensemble of converged solutions for the planar chaotic three-body problem obtained using an arbitrarily precise numerical integrator can be used to train a deep artificial neural network (ANN) that, over a bounded time interval, provides accurate solutions at a fixed computational cost and up to 100 million times faster than the numerical integrator. In addition, we demonstrate the importance of training an ANN using converged solutions from an arbitrary precise integrator, relative to solutions computed by a conventional fixed precision integrator, which can introduce errors in the training data, due to numerical round-off and time discretization, that are learned by the ANN. Our results provide evidence that, for computationally challenging regions of phase space, a trained ANN can replace existing numerical solvers, enabling fast and scalable simulations of many-body systems to shed light on outstanding phenomena such as the formation of black hole binary systems or the origin of the core collapse in dense star clusters. |
| spellingShingle | Breen, PG Foley, CN Boekholt, T Zwart, SP Newton versus the machine: solving the chaotic three-body problem using deep neural networks |
| title | Newton versus the machine: solving the chaotic three-body problem using deep neural networks |
| title_full | Newton versus the machine: solving the chaotic three-body problem using deep neural networks |
| title_fullStr | Newton versus the machine: solving the chaotic three-body problem using deep neural networks |
| title_full_unstemmed | Newton versus the machine: solving the chaotic three-body problem using deep neural networks |
| title_short | Newton versus the machine: solving the chaotic three-body problem using deep neural networks |
| title_sort | newton versus the machine solving the chaotic three body problem using deep neural networks |
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