Modeling chaotic Lorenz ODE System using Scientific Machine Learning

• URL: http://arxiv.org/abs/2410.06452v1
• Date: Wed, 9 Oct 2024 01:17:06 GMT
• Title: Modeling chaotic Lorenz ODE System using Scientific Machine Learning
• Authors: Sameera S Kashyap, Raj Abhijit Dandekar, Rajat Dandekar, Sreedath Panat,
• Abstract summary: In this paper, we have integrated Scientific Machine Learning (SciML) methods into foundational weather models. By combining the interpretability of physical climate models with the computational power of neural networks, SciML models can prove to be a reliable tool for modeling climate.
• Score: 1.4633779950109127
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In climate science, models for global warming and weather prediction face significant challenges due to the limited availability of high-quality data and the difficulty in obtaining it, making data efficiency crucial. In the past few years, Scientific Machine Learning (SciML) models have gained tremendous traction as they can be trained in a data-efficient manner, making them highly suitable for real-world climate applications. Despite this, very little attention has been paid to chaotic climate system modeling utilizing SciML methods. In this paper, we have integrated SciML methods into foundational weather models, where we have enhanced large-scale climate predictions with a physics-informed approach that achieves high accuracy with reduced data. We successfully demonstrate that by combining the interpretability of physical climate models with the computational power of neural networks, SciML models can prove to be a reliable tool for modeling climate. This indicates a shift from the traditional black box-based machine learning modeling of climate systems to physics-informed decision-making, leading to effective climate policy implementation.

Related papers

• Robustness of AI-based weather forecasts in a changing climate [1.4779266690741741]
  We show that current state-of-the-art machine learning models trained for weather forecasting in present-day climate produce skillful forecasts across different climate states. Despite current limitations, our results suggest that data-driven machine learning models will provide powerful tools for climate science.
  arXiv  Detail & Related papers  (2024-09-27T08:11:49Z)
• Efficient Localized Adaptation of Neural Weather Forecasting: A Case Study in the MENA Region [62.09891513612252]
  We focus on limited-area modeling and train our model specifically for localized region-level downstream tasks. We consider the MENA region due to its unique climatic challenges, where accurate localized weather forecasting is crucial for managing water resources, agriculture and mitigating the impacts of extreme weather events. Our study aims to validate the effectiveness of integrating parameter-efficient fine-tuning (PEFT) methodologies, specifically Low-Rank Adaptation (LoRA) and its variants, to enhance forecast accuracy, as well as training speed, computational resource utilization, and memory efficiency in weather and climate modeling for specific regions.
  arXiv  Detail & Related papers  (2024-09-11T19:31:56Z)
• MambaDS: Near-Surface Meteorological Field Downscaling with Topography Constrained Selective State Space Modeling [68.69647625472464]
  Downscaling, a crucial task in meteorological forecasting, enables the reconstruction of high-resolution meteorological states for target regions. Previous downscaling methods lacked tailored designs for meteorology and encountered structural limitations. We propose a novel model called MambaDS, which enhances the utilization of multivariable correlations and topography information.
  arXiv  Detail & Related papers  (2024-08-20T13:45:49Z)
• ClimODE: Climate and Weather Forecasting with Physics-informed Neural ODEs [14.095897879222676]
  We present ClimODE, a continuous-time process that implements key principle of statistical mechanics. ClimODE models precise weather evolution with value-conserving dynamics, learning global weather transport as a neural flow. Our approach outperforms existing data-driven methods in global, regional forecasting with an order of magnitude smaller parameterization.
  arXiv  Detail & Related papers  (2024-04-15T06:38:21Z)
• Towards Causal Representations of Climate Model Data [18.82507552857727]
  This work delves into the potential of causal representation learning, specifically the emphCausal Discovery with Single-parent Decoding (CDSD) method. Our findings shed light on the challenges, limitations, and promise of using CDSD as a stepping stone towards more interpretable and robust climate model emulation.
  arXiv  Detail & Related papers  (2023-12-05T16:13:34Z)
• 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)
• Climate Intervention Analysis using AI Model Guided by Statistical Physics Principles [6.824166358727082]
  We propose a novel solution by utilizing a principle from statistical physics known as the Fluctuation-Dissipation Theorem (FDT) By leveraging, we are able to extract information encoded in a large dataset produced by Earth System Models. Our model, AiBEDO, is capable of capturing the complex, multi-timescale effects of radiation perturbations on global and regional surface climate.
  arXiv  Detail & Related papers  (2023-02-07T05:09:10Z)
• ClimaX: A foundation model for weather and climate [51.208269971019504]
  ClimaX is a deep learning model for weather and climate science. It can be pre-trained with a self-supervised learning objective on climate datasets. It can be fine-tuned to address a breadth of climate and weather tasks.
  arXiv  Detail & Related papers  (2023-01-24T23:19:01Z)
• Multi-scale Digital Twin: Developing a fast and physics-informed surrogate model for groundwater contamination with uncertain climate models [53.44486283038738]
  Climate change exacerbates the long-term soil management problem of groundwater contamination. We develop a physics-informed machine learning surrogate model using U-Net enhanced Fourier Neural Contaminated (PDENO) In parallel, we develop a convolutional autoencoder combined with climate data to reduce the dimensionality of climatic region similarities across the United States.
  arXiv  Detail & Related papers  (2022-11-20T06:46:35Z)
• 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)
• Climate-Invariant Machine Learning [0.8831201550856289]
  Current climate models require representations of processes that occur at scales smaller than model grid size. Recent machine learning (ML) algorithms hold promise to improve such process representations, but tend to extrapolate poorly to climate regimes they were not trained on. We propose a new framework - termed "climate-invariant" ML - incorporating knowledge of climate processes into ML algorithms.
  arXiv  Detail & Related papers  (2021-12-14T07:02:57Z)

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.