Machine Learning for Discovering Effective Interaction Kernels between Celestial Bodies from Ephemerides

• URL: http://arxiv.org/abs/2108.11894v1
• Date: Thu, 26 Aug 2021 16:30:59 GMT
• Title: Machine Learning for Discovering Effective Interaction Kernels between Celestial Bodies from Ephemerides
• Authors: Ming Zhong, Jason Miller, Mauro Maggioni
• Abstract summary: We use a data-driven learning approach to derive a stable and accurate model for the motion of celestial bodies in our Solar System. By modeling the major astronomical bodies in the Solar System as pairwise interacting agents, our learned model generate extremely accurate dynamics. Our model can provide a unified explanation to the observation data, especially in terms of reproducing the perihelion precession of Mars, Mercury, and the Moon.
• Score: 10.77689830299308
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Building accurate and predictive models of the underlying mechanisms of celestial motion has inspired fundamental developments in theoretical physics. Candidate theories seek to explain observations and predict future positions of planets, stars, and other astronomical bodies as faithfully as possible. We use a data-driven learning approach, extending that developed in Lu et al. ($2019$) and extended in Zhong et al. ($2020$), to a derive stable and accurate model for the motion of celestial bodies in our Solar System. Our model is based on a collective dynamics framework, and is learned from the NASA Jet Propulsion Lab's development ephemerides. By modeling the major astronomical bodies in the Solar System as pairwise interacting agents, our learned model generate extremely accurate dynamics that preserve not only intrinsic geometric properties of the orbits, but also highly sensitive features of the dynamics, such as perihelion precession rates. Our learned model can provide a unified explanation to the observation data, especially in terms of reproducing the perihelion precession of Mars, Mercury, and the Moon. Moreover, Our model outperforms Newton's Law of Universal Gravitation in all cases and performs similarly to, and exceeds on the Moon, the Einstein-Infeld-Hoffman equations derived from Einstein's theory of general relativity.

Related papers

• Using Galaxy Evolution as Source of Physics-Based Ground Truth for Generative Models [0.9701233658865522]
  We build a conditional denoising diffusionaxy probabilistic model (DDPM) and a conditional variational autoencoder (CVAE) This is one of the first studies to probe these generative models using physically motivated metrics. We find that both models produce comparable realistic galaxies based on human evaluation, but our physics-based metrics are better able to discern the strengths and weaknesses of the generative models.
  arXiv  Detail & Related papers  (2024-07-09T21:01:08Z)
• Aurora: A Foundation Model of the Atmosphere [56.97266186291677]
  We introduce Aurora, a large-scale foundation model of the atmosphere trained on over a million hours of diverse weather and climate data. In under a minute, Aurora produces 5-day global air pollution predictions and 10-day high-resolution weather forecasts.
  arXiv  Detail & Related papers  (2024-05-20T14:45:18Z)
• LLM and Simulation as Bilevel Optimizers: A New Paradigm to Advance Physical Scientific Discovery [141.39722070734737]
  We propose to enhance the knowledge-driven, abstract reasoning abilities of Large Language Models with the computational strength of simulations. We introduce Scientific Generative Agent (SGA), a bilevel optimization framework. We conduct experiments to demonstrate our framework's efficacy in law discovery and molecular design.
  arXiv  Detail & Related papers  (2024-05-16T03:04:10Z)
• Predictive World Models from Real-World Partial Observations [66.80340484148931]
  We present a framework for learning a probabilistic predictive world model for real-world road environments. While prior methods require complete states as ground truth for learning, we present a novel sequential training method to allow HVAEs to learn to predict complete states from partially observed states only.
  arXiv  Detail & Related papers  (2023-01-12T02:07:26Z)
• Deep Learning for Space Weather Prediction: Bridging the Gap between Heliophysics Data and Theory [3.5597536699796795]
  Deep learning technology will produce a new class of predictively powerful space weather models. We call on NASA to invest in the research and infrastructure necessary for the heliophysics' community to take advantage of these advances.
  arXiv  Detail & Related papers  (2022-12-27T00:30:39Z)
• Astronomia ex machina: a history, primer, and outlook on neural networks in astronomy [0.0]
  We trace the evolution of connectionism in astronomy through its three waves. We argue for the adoption of GPT-like foundation models fine-tuned for astronomical applications.
  arXiv  Detail & Related papers  (2022-11-07T19:00:00Z)
• Data-driven discovery of non-Newtonian astronomy via learning non-Euclidean Hamiltonian [23.309368900269565]
  We present a method for data-driven discovery of non-Newtonian astronomy. Preliminary results show the importance of both these properties in training stability and prediction accuracy.
  arXiv  Detail & Related papers  (2022-09-30T20:59:42Z)
• Rediscovering orbital mechanics with machine learning [1.2999518604217852]
  We train a "graph neural network" to simulate the dynamics of our solar system's Sun, planets, and large moons from 30 years of trajectory data. We then use symbolic regression to discover an analytical expression for the force law implicitly learned by the neural network.
  arXiv  Detail & Related papers  (2022-02-04T18:44:21Z)
• The problem of engines in statistical physics [62.997667081978825]
  Engines are open systems that can generate work cyclically, at the expense of an external disequilibrium. Recent advances in the theory of open quantum systems point to a more realistic description of autonomous engines. We show how the external loading force and the thermal noise may be incorporated into the relevant equations of motion.
  arXiv  Detail & Related papers  (2021-08-17T03:59:09Z)
• GEM: Group Enhanced Model for Learning Dynamical Control Systems [78.56159072162103]
  We build effective dynamical models that are amenable to sample-based learning. We show that learning the dynamics on a Lie algebra vector space is more effective than learning a direct state transition model. This work sheds light on a connection between learning of dynamics and Lie group properties, which opens doors for new research directions.
  arXiv  Detail & Related papers  (2021-04-07T01:08:18Z)
• Physics-Integrated Variational Autoencoders for Robust and Interpretable Generative Modeling [86.9726984929758]
  We focus on the integration of incomplete physics models into deep generative models. We propose a VAE architecture in which a part of the latent space is grounded by physics. We demonstrate generative performance improvements over a set of synthetic and real-world datasets.
  arXiv  Detail & Related papers  (2021-02-25T20:28:52Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.