Harnessing expressive capacity of Machine Learning modeling to represent complex coupling of Earth's auroral space weather regimes

• URL: http://arxiv.org/abs/2111.14998v1
• Date: Mon, 29 Nov 2021 22:35:09 GMT
• Title: Harnessing expressive capacity of Machine Learning modeling to represent complex coupling of Earth's auroral space weather regimes
• Authors: Jack Ziegler and Ryan M. Mcgranaghan
• Abstract summary: We develop multiple Deep Learning (DL) models that advance predictions of the global auroral particle precipitation. We use observations from low Earth orbiting spacecraft of electron energy flux to develop a model that improves global nowcasts. Notably, the ML models improve prediction of the extreme events, historically to accurate specification and indicate that increased capacity provided by ML innovation can address grand challenges in science of space weather.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We develop multiple Deep Learning (DL) models that advance the state-of-the-art predictions of the global auroral particle precipitation. We use observations from low Earth orbiting spacecraft of the electron energy flux to develop a model that improves global nowcasts (predictions at the time of observation) of the accelerated particles. Multiple Machine Learning (ML) modeling approaches are compared, including a novel multi-task model, models with tail- and distribution-based loss functions, and a spatio-temporally sparse 2D-convolutional model. We detail the data preparation process as well as the model development that will be illustrative for many similar time series global regression problems in space weather and across domains. Our ML improvements are three-fold: 1) loss function engineering; 2) multi-task learning; and 3) transforming the task from time series prediction to spatio-temporal prediction. Notably, the ML models improve prediction of the extreme events, historically obstinate to accurate specification and indicate that increased expressive capacity provided by ML innovation can address grand challenges in the science of space weather.

Related papers

• Prithvi WxC: Foundation Model for Weather and Climate [2.9230020115516253]
  Prithvi WxC is a 2.3 billion parameter foundation model developed using 160 variables from the Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2). The model has been designed to accommodate large token counts to model weather phenomena in different topologies at fine resolutions. We test the model on a set of challenging downstream tasks namely: Autoregressive rollout forecasting, Downscaling, Gravity wave flux parameterization, and Extreme events estimation.
  arXiv  Detail & Related papers  (2024-09-20T15:53:17Z)
• Physics-Informed Machine Learning Towards A Real-Time Spacecraft Thermal Simulator [15.313871831214902]
  The PIML model or hybrid model presented here consists of a neural network which predicts reduced nodalizations given on-orbit thermal load conditions. We compare the computational performance and accuracy of the hybrid model to a data-driven neural net model, and a high-fidelity finite-difference model of a prototype Earth-orbiting small spacecraft. The PIML based active nodalization approach provides significantly better generalization than the neural net model and coarse mesh model, while reducing computing cost by up to 1.7x compared to the high-fidelity model.
  arXiv  Detail & Related papers  (2024-07-08T16:38:52Z)
• Generalizing Weather Forecast to Fine-grained Temporal Scales via Physics-AI Hybrid Modeling [55.13352174687475]
  This paper proposes a physics-AI hybrid model (i.e., WeatherGFT) which Generalizes weather forecasts to Finer-grained Temporal scales. Specifically, we employ a carefully designed PDE kernel to simulate physical evolution on a small time scale. We introduce a lead time-aware training framework to promote the generalization of the model at different lead times.
  arXiv  Detail & Related papers  (2024-05-22T16:21:02Z)
• Equivariant Graph Neural Operator for Modeling 3D Dynamics [148.98826858078556]
  We propose Equivariant Graph Neural Operator (EGNO) to directly models dynamics as trajectories instead of just next-step prediction. EGNO explicitly learns the temporal evolution of 3D dynamics where we formulate the dynamics as a function over time and learn neural operators to approximate it. Comprehensive experiments in multiple domains, including particle simulations, human motion capture, and molecular dynamics, demonstrate the significantly superior performance of EGNO against existing methods.
  arXiv  Detail & Related papers  (2024-01-19T21:50:32Z)
• Residual Corrective Diffusion Modeling for Km-scale Atmospheric Downscaling [58.456404022536425]
  State of the art for physical hazard prediction from weather and climate requires expensive km-scale numerical simulations driven by coarser resolution global inputs. Here, a generative diffusion architecture is explored for downscaling such global inputs to km-scale, as a cost-effective machine learning alternative. The model is trained to predict 2km data from a regional weather model over Taiwan, conditioned on a 25km global reanalysis.
  arXiv  Detail & Related papers  (2023-09-24T19:57:22Z)
• Learning Controllable Adaptive Simulation for Multi-resolution Physics [86.8993558124143]
  We introduce Learning controllable Adaptive simulation for Multi-resolution Physics (LAMP) as the first full deep learning-based surrogate model. LAMP consists of a Graph Neural Network (GNN) for learning the forward evolution, and a GNN-based actor-critic for learning the policy of spatial refinement and coarsening. We demonstrate that our LAMP outperforms state-of-the-art deep learning surrogate models, and can adaptively trade-off computation to improve long-term prediction error.
  arXiv  Detail & Related papers  (2023-05-01T23:20:27Z)
• A machine learning and feature engineering approach for the prediction of the uncontrolled re-entry of space objects [1.0205541448656992]
  We present the development of a deep learning model for the re-entry prediction of uncontrolled objects in Low Earth Orbit (LEO) The model is based on a modified version of the Sequence-to-Sequence architecture and is trained on the average altitude profile as derived from a set of Two-Line Element (TLE) data of over 400 bodies. The novelty of the work consists in introducing in the deep learning model, alongside the average altitude, three new input features: a drag-like coefficient (B*), the average solar index, and the area-to-mass ratio of the object.
  arXiv  Detail & Related papers  (2023-03-17T13:53:59Z)
• 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)
• Data-driven low-dimensional dynamic model of Kolmogorov flow [0.0]
  Reduced order models (ROMs) that capture flow dynamics are of interest for decreasing computational costs for simulation. This work presents a data-driven framework for minimal-dimensional models that effectively capture the dynamics and properties of the flow. We apply this to Kolmogorov flow in a regime consisting of chaotic and intermittent behavior.
  arXiv  Detail & Related papers  (2022-10-29T23:05:39Z)
• Multivariate Time Series Forecasting with Dynamic Graph Neural ODEs [65.18780403244178]
  We propose a continuous model to forecast Multivariate Time series with dynamic Graph neural Ordinary Differential Equations (MTGODE) Specifically, we first abstract multivariate time series into dynamic graphs with time-evolving node features and unknown graph structures. Then, we design and solve a neural ODE to complement missing graph topologies and unify both spatial and temporal message passing.
  arXiv  Detail & Related papers  (2022-02-17T02:17:31Z)
• Automating Turbulence Modeling by Multi-Agent Reinforcement Learning [4.784658158364452]
  We introduce multi-agent reinforcement learning as an automated discovery tool of turbulence models. We demonstrate the potential of this approach on Large Eddy Simulations of homogeneous and isotropic turbulence.
  arXiv  Detail & Related papers  (2020-05-18T18:45:09Z)

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.