Long-term instabilities of deep learning-based digital twins of the climate system: The cause and a solution

• URL: http://arxiv.org/abs/2304.07029v1
• Date: Fri, 14 Apr 2023 09:49:11 GMT
• Title: Long-term instabilities of deep learning-based digital twins of the climate system: The cause and a solution
• Authors: Ashesh Chattopadhyay and Pedram Hassanzadeh
• Abstract summary: Long-term stability is a critical property for deep learning-based data-driven digital twins of the Earth system. We show how turbulence physics and the absence of convergence in deep learning-based time-integrators amplify this bias leading to unstable error propagation. We develop long-term stable data-driven digital twins for the climate system and demonstrate accurate short-term forecasts, and hundreds of years of long-term stable time-integration with accurate mean and variability.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Long-term stability is a critical property for deep learning-based data-driven digital twins of the Earth system. Such data-driven digital twins enable sub-seasonal and seasonal predictions of extreme environmental events, probabilistic forecasts, that require a large number of ensemble members, and computationally tractable high-resolution Earth system models where expensive components of the models can be replaced with cheaper data-driven surrogates. Owing to computational cost, physics-based digital twins, though long-term stable, are intractable for real-time decision-making. Data-driven digital twins offer a cheaper alternative to them and can provide real-time predictions. However, such digital twins can only provide short-term forecasts accurately since they become unstable when time-integrated beyond 20 days. Currently, the cause of the instabilities is unknown, and the methods that are used to improve their stability horizons are ad-hoc and lack rigorous theory. In this paper, we reveal that the universal causal mechanism for these instabilities in any turbulent flow is due to \textit{spectral bias} wherein, \textit{any} deep learning architecture is biased to learn only the large-scale dynamics and ignores the small scales completely. We further elucidate how turbulence physics and the absence of convergence in deep learning-based time-integrators amplify this bias leading to unstable error propagation. Finally, using the quasigeostrophic flow and ECMWF Reanalysis data as test cases, we bridge the gap between deep learning theory and fundamental numerical analysis to propose one mitigative solution to such instabilities. We develop long-term stable data-driven digital twins for the climate system and demonstrate accurate short-term forecasts, and hundreds of years of long-term stable time-integration with accurate mean and variability.

Related papers

• Deep End-to-End Survival Analysis with Temporal Consistency [49.77103348208835]
  We present a novel Survival Analysis algorithm designed to efficiently handle large-scale longitudinal data. A central idea in our method is temporal consistency, a hypothesis that past and future outcomes in the data evolve smoothly over time. Our framework uniquely incorporates temporal consistency into large datasets by providing a stable training signal.
  arXiv  Detail & Related papers  (2024-10-09T11:37:09Z)
• A probabilistic framework for learning non-intrusive corrections to long-time climate simulations from short-time training data [12.566163525039558]
  We present a strategy for training neural network models to non-intrusively correct under-resolved long-time simulations of chaotic systems. We demonstrate its ability to accurately predict the anisotropic statistics over time horizons more than 30 times longer than the data seen in training.
  arXiv  Detail & Related papers  (2024-08-02T18:34:30Z)
• 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)
• Long-term drought prediction using deep neural networks based on geospatial weather data [75.38539438000072]
  High-quality drought forecasting up to a year in advance is critical for agriculture planning and insurance. We tackle drought data by introducing an end-to-end approach that adopts a systematic end-to-end approach. Key findings are the exceptional performance of a Transformer model, EarthFormer, in making accurate short-term (up to six months) forecasts.
  arXiv  Detail & Related papers  (2023-09-12T13:28:06Z)
• Benchmarking Autoregressive Conditional Diffusion Models for Turbulent Flow Simulation [29.806100463356906]
  We analyze if fully data-driven fluid solvers that utilize an autoregressive rollout based on conditional diffusion models are a viable option. We investigate accuracy, posterior sampling, spectral behavior, and temporal stability, while requiring that methods generalize to flow parameters beyond the training regime. We find that even simple diffusion-based approaches can outperform multiple established flow prediction methods in terms of accuracy and temporal stability, while being on par with state-of-the-art stabilization techniques like unrolling at training time.
  arXiv  Detail & Related papers  (2023-09-04T18:01:42Z)
• Discovering Predictable Latent Factors for Time Series Forecasting [39.08011991308137]
  We develop a novel framework for inferring the intrinsic latent factors implied by the observable time series. We introduce three characteristics, i.e., predictability, sufficiency, and identifiability, and model these characteristics via the powerful deep latent dynamics models. Empirical results on multiple real datasets show the efficiency of our method for different kinds of time series forecasting.
  arXiv  Detail & Related papers  (2023-03-18T14:37:37Z)
• Leveraging the structure of dynamical systems for data-driven modeling [111.45324708884813]
  We consider the impact of the training set and its structure on the quality of the long-term prediction. We show how an informed design of the training set, based on invariants of the system and the structure of the underlying attractor, significantly improves the resulting models.
  arXiv  Detail & Related papers  (2021-12-15T20:09:20Z)
• Physics-aware, probabilistic model order reduction with guaranteed stability [0.0]
  We propose a generative framework for learning an effective, lower-dimensional, coarse-grained dynamical model. We demonstrate its efficacy and accuracy in multiscale physical systems of particle dynamics.
  arXiv  Detail & Related papers  (2021-01-14T19:16:51Z)
• From Goals, Waypoints & Paths To Long Term Human Trajectory Forecasting [54.273455592965355]
  Uncertainty in future trajectories stems from two sources: (a) sources known to the agent but unknown to the model, such as long term goals and (b)sources that are unknown to both the agent & the model, such as intent of other agents & irreducible randomness indecisions. We model the epistemic un-certainty through multimodality in long term goals and the aleatoric uncertainty through multimodality in waypoints& paths. To exemplify this dichotomy, we also propose a novel long term trajectory forecasting setting, with prediction horizons upto a minute, an order of magnitude longer than prior works.
  arXiv  Detail & Related papers  (2020-12-02T21:01:29Z)
• Modeling Atmospheric Data and Identifying Dynamics: Temporal Data-Driven Modeling of Air Pollutants [2.578242050187029]
  We present an empirical approach using data-driven techniques to study air quality in Madrid. We find parsimonious systems of ordinary differential equations that model the concentration of pollutants and their changes over time. Our results show that Akaike's Information Criterion can work well in conjunction with best subset regression as to find an equilibrium between sparsity and goodness of fit.
  arXiv  Detail & Related papers  (2020-10-13T16:46:07Z)
• Stochastically forced ensemble dynamic mode decomposition for forecasting and analysis of near-periodic systems [65.44033635330604]
  We introduce a novel load forecasting method in which observed dynamics are modeled as a forced linear system. We show that its use of intrinsic linear dynamics offers a number of desirable properties in terms of interpretability and parsimony. Results are presented for a test case using load data from an electrical grid.
  arXiv  Detail & Related papers  (2020-10-08T20:25:52Z)

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.