Machine learning-accelerated chemistry modeling of protoplanetary disks

• URL: http://arxiv.org/abs/2209.13336v1
• Date: Tue, 27 Sep 2022 12:42:13 GMT
• Title: Machine learning-accelerated chemistry modeling of protoplanetary disks
• Authors: Grigorii V. Smirnov-Pinchukov, Tamara Molyarova, Dmitry A. Semenov, Vitaly V. Akimkin, Sierk van Terwisga, Riccardo Francheschi, Thomas Henning
• Abstract summary: We used a thermo-chemical modeling code to generate a diverse population of protoplanetary disk models. We trained a K-nearest neighbors regressor to instantly predict the chemistry of other disk models.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Aims. With the large amount of molecular emission data from (sub)millimeter observatories and incoming James Webb Space Telescope infrared spectroscopy, access to fast forward models of the chemical composition of protoplanetary disks is of paramount importance. Methods. We used a thermo-chemical modeling code to generate a diverse population of protoplanetary disk models. We trained a K-nearest neighbors (KNN) regressor to instantly predict the chemistry of other disk models. Results. We show that it is possible to accurately reproduce chemistry using just a small subset of physical conditions, thanks to correlations between the local physical conditions in adopted protoplanetary disk models. We discuss the uncertainties and limitations of this method. Conclusions. The proposed method can be used for Bayesian fitting of the line emission data to retrieve disk properties from observations. We present a pipeline for reproducing the same approach on other disk chemical model sets.

Related papers

• Disentangling CO Chemistry in a Protoplanetary Disk Using Explanatory Machine Learning Techniques [0.0]
  Molecular abundances in protoplanetary disks are highly sensitive to the local physical conditions. We present a new approach to understanding these chemical and physical interdependencies using machine learning.
  arXiv  Detail & Related papers  (2023-12-08T18:59:22Z)
• A Heat Diffusion Perspective on Geodesic Preserving Dimensionality Reduction [66.21060114843202]
  We propose a more general heat kernel based manifold embedding method that we call heat geodesic embeddings. Results show that our method outperforms existing state of the art in preserving ground truth manifold distances. We also showcase our method on single cell RNA-sequencing datasets with both continuum and cluster structure.
  arXiv  Detail & Related papers  (2023-05-30T13:58:50Z)
• Applications of Gaussian Processes at Extreme Lengthscales: From Molecules to Black Holes [4.18804572788063]
  This thesis aims to use GP modelling to reason about the latent emission signature from the Seyfert galaxy Markarian 335. The second contribution is to extend the GP framework to molecular and chemical reaction representations and to provide an open-source software library to enable the framework to be used by scientists. The fourth contribution is to introduce a Bayesian optimisation scheme capable of modelling aleatoric uncertainty to facilitate the identification of material compositions that possess intrinsic robustness to large scale fabrication processes.
  arXiv  Detail & Related papers  (2023-03-24T22:20:14Z)
• Generative structured normalizing flow Gaussian processes applied to spectroscopic data [4.0773490083614075]
  In the physical sciences, limited training data may not adequately characterize future observed data. It is critical that models adequately indicate uncertainty, particularly when they may be asked to extrapolate. We demonstrate the methodology on laser-induced breakdown spectroscopy data from the ChemCam instrument onboard the Mars rover Curiosity.
  arXiv  Detail & Related papers  (2022-12-14T23:57:46Z)
• Spatiotemporal modeling of European paleoclimate using doubly sparse Gaussian processes [61.31361524229248]
  We build on recent scale sparsetemporal GPs to reduce the computational burden. We successfully employ such a doubly sparse GP to construct a probabilistic model of paleoclimate.
  arXiv  Detail & Related papers  (2022-11-15T14:15:04Z)
• Local manifold learning and its link to domain-based physics knowledge [53.15471241298841]
  In many reacting flow systems, the thermo-chemical state-space is assumed to evolve close to a low-dimensional manifold (LDM) We show that PCA applied in local clusters of data (local PCA) is capable of detecting the intrinsic parameterization of the thermo-chemical state-space.
  arXiv  Detail & Related papers  (2022-07-01T09:06:25Z)
• Deep Learning Models of the Discrete Component of the Galactic Interstellar Gamma-Ray Emission [61.26321023273399]
  A significant point-like component from the small scale (or discrete) structure in the H2 interstellar gas might be present in the Fermi-LAT data. We show that deep learning may be effectively employed to model the gamma-ray emission traced by these rare H2 proxies within statistical significance in data-rich regions.
  arXiv  Detail & Related papers  (2022-06-06T18:00:07Z)
• Learning black- and gray-box chemotactic PDEs/closures from agent based Monte Carlo simulation data [0.6882042556551611]
  We propose a machine learning framework for the data-driven discovery of macroscopic chemotactic Partial Differential Equations (PDEs) The fine scale, detailed, hybrid (continuum - Monte Carlo) simulation model embodies the underlying biophysics. We learn effective, coarse-grained "Keller-Segel class" chemotactic PDEs using machine learning regressors.
  arXiv  Detail & Related papers  (2022-05-26T03:02:49Z)
• Prediction of liquid fuel properties using machine learning models with Gaussian processes and probabilistic conditional generative learning [56.67751936864119]
  The present work aims to construct cheap-to-compute machine learning (ML) models to act as closure equations for predicting the physical properties of alternative fuels. Those models can be trained using the database from MD simulations and/or experimental measurements in a data-fusion-fidelity approach. The results show that ML models can predict accurately the fuel properties of a wide range of pressure and temperature conditions.
  arXiv  Detail & Related papers  (2021-10-18T14:43:50Z)
• Visualizing spinon Fermi surfaces with time-dependent spectroscopy [62.997667081978825]
  We propose applying time-dependent photo-emission spectroscopy, an established tool in solid state systems, in cold atom quantum simulators. We show in exact diagonalization simulations of the one-dimensional $t-J$ model that the spinons start to populate previously unoccupied states in an effective band structure. The dependence of the spectral function on the time after the pump pulse reveals collective interactions among spinons.
  arXiv  Detail & Related papers  (2021-05-27T18:00:02Z)
• Using Restricted Boltzmann Machines to Model Molecular Geometries [0.0]
  This paper proposes a new methodology for modeling a set of physical parameters by taking advantage of the Boltzmann machine's fast learning capacity and representational power. In this paper we introduce a new RBM based on the Tanh activation function, and conduct a comparison of RBMs with different activation functions. We demonstrate the ability of Gaussian RBMs to model small molecules such as water and ethane.
  arXiv  Detail & Related papers  (2020-12-13T07:02:32Z)

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.