Unsupervised classification of fully kinetic simulations of plasmoid instability using Self-Organizing Maps (SOMs)

• URL: http://arxiv.org/abs/2304.13469v1
• Date: Wed, 26 Apr 2023 11:47:05 GMT
• Title: Unsupervised classification of fully kinetic simulations of plasmoid instability using Self-Organizing Maps (SOMs)
• Authors: Sophia K\"ohne, Elisabetta Boella, Maria Elena Innocenti
• Abstract summary: We apply a clustering method based on Self-Organizing Maps to fully kinetic simulations of plasmoid instability. We obtain clusters that map well, a posteriori, to our knowledge of the process. The method appears as a promising option for the analysis of data, both from simulations and from observations.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The growing amount of data produced by simulations and observations of space physics processes encourages the use of methods rooted in Machine Learning for data analysis and physical discovery. We apply a clustering method based on Self-Organizing Maps (SOM) to fully kinetic simulations of plasmoid instability, with the aim of assessing its suitability as a reliable analysis tool for both simulated and observed data. We obtain clusters that map well, a posteriori, to our knowledge of the process: the clusters clearly identify the inflow region, the inner plasmoid region, the separatrices, and regions associated with plasmoid merging. SOM-specific analysis tools, such as feature maps and Unified Distance Matrix, provide one with valuable insights into both the physics at work and specific spatial regions of interest. The method appears as a promising option for the analysis of data, both from simulations and from observations, and could also potentially be used to trigger the switch to different simulation models or resolution in coupled codes for space simulations.

Related papers

• Synthetic location trajectory generation using categorical diffusion models [50.809683239937584]
  Diffusion models (DPMs) have rapidly evolved to be one of the predominant generative models for the simulation of synthetic data. We propose using DPMs for the generation of synthetic individual location trajectories (ILTs) which are sequences of variables representing physical locations visited by individuals.
  arXiv  Detail & Related papers  (2024-02-19T15:57:39Z)
• Uncertainty-biased molecular dynamics for learning uniformly accurate interatomic potentials [20.574068788623798]
  Active learning (AL) uses either biased or unbiased molecular dynamics (MD) simulations to generate candidate pools. We use the proposed uncertainty-driven AL approach to develop machine-learned interatomic potentials (MLIPs) for two benchmark systems. MLIPs trained with the proposed data-generation method more accurately represent the relevant configurational space for both atomic systems.
  arXiv  Detail & Related papers  (2023-12-03T14:39:14Z)
• Simulation-based inference using surjective sequential neural likelihood estimation [50.24983453990065]
  Surjective Sequential Neural Likelihood estimation is a novel method for simulation-based inference. By embedding the data in a low-dimensional space, SSNL solves several issues previous likelihood-based methods had when applied to high-dimensional data sets.
  arXiv  Detail & Related papers  (2023-08-02T10:02:38Z)
• Neural FIM for learning Fisher Information Metrics from point cloud data [71.07939200676199]
  We propose neural FIM, a method for computing the Fisher information metric (FIM) from point cloud data. We demonstrate its utility in selecting parameters for the PHATE visualization method as well as its ability to obtain information pertaining to local volume illuminating branching points and cluster centers embeddings of a toy dataset and two single-cell datasets of IPSC reprogramming and PBMCs (immune cells)
  arXiv  Detail & Related papers  (2023-06-01T17:36:13Z)
• Learning Similarity Metrics for Volumetric Simulations with Multiscale CNNs [25.253880881581956]
  We propose a similarity model based on entropy, which allows for the creation of physically meaningful ground truth distances. We create collections of fields from numerical PDE solvers and existing simulation data repositories. A multiscale CNN architecture that computes a volumetric similarity metric (VolSiM) is proposed.
  arXiv  Detail & Related papers  (2022-02-08T19:19:08Z)
• Scalable Intervention Target Estimation in Linear Models [52.60799340056917]
  Current approaches to causal structure learning either work with known intervention targets or use hypothesis testing to discover the unknown intervention targets. This paper proposes a scalable and efficient algorithm that consistently identifies all intervention targets. The proposed algorithm can be used to also update a given observational Markov equivalence class into the interventional Markov equivalence class.
  arXiv  Detail & Related papers  (2021-11-15T03:16:56Z)
• Likelihood-Free Inference in State-Space Models with Unknown Dynamics [71.94716503075645]
  We introduce a method for inferring and predicting latent states in state-space models where observations can only be simulated, and transition dynamics are unknown. We propose a way of doing likelihood-free inference (LFI) of states and state prediction with a limited number of simulations.
  arXiv  Detail & Related papers  (2021-11-02T12:33:42Z)
• Unsupervised classification of simulated magnetospheric regions [0.0]
  In magnetospheric missions, burst mode data sampling should be triggered in the presence of processes of scientific or operational interest. We present an unsupervised classification method for magnetospheric regions, that could constitute the first-step of a multi-step method for the automatic identification of magnetospheric processes of interest.
  arXiv  Detail & Related papers  (2021-09-10T14:57:32Z)
• Scalable nonparametric Bayesian learning for heterogeneous and dynamic velocity fields [8.744017403796406]
  We develop a model for learning heterogeneous and dynamic patterns of velocity field data. We show the effectiveness of our techniques to the NGSIM dataset of complex multi-vehicle interactions.
  arXiv  Detail & Related papers  (2021-02-15T17:45:46Z)
• Surface Warping Incorporating Machine Learning Assisted Domain Likelihood Estimation: A New Paradigm in Mine Geology Modelling and Automation [68.8204255655161]
  A Bayesian warping technique has been proposed to reshape modeled surfaces based on geochemical and spatial constraints imposed by newly acquired blasthole data. This paper focuses on incorporating machine learning in this warping framework to make the likelihood generalizable. Its foundation is laid by a Bayesian computation in which the geological domain likelihood given the chemistry, p(g|c) plays a similar role to p(y(c)|g.
  arXiv  Detail & Related papers  (2021-02-15T10:37:52Z)
• Using Deep Learning to Explore Local Physical Similarity for Global-scale Bridging in Thermal-hydraulic Simulation [4.350727579753697]
  Current system thermal-hydraulic codes have limited credibility in simulating real plant conditions. This paper proposes a data-driven approach, Feature Similarity Measurement FFSM, to overcome these difficulties. Deep learning is applied to construct and explore the relationship between the local physical features and simulation errors.
  arXiv  Detail & Related papers  (2020-01-06T20:14:46Z)

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.