Stochastic Parameterizations: Better Modelling of Temporal Correlations using Probabilistic Machine Learning

• URL: http://arxiv.org/abs/2203.14814v1
• Date: Mon, 28 Mar 2022 14:51:42 GMT
• Title: Stochastic Parameterizations: Better Modelling of Temporal Correlations using Probabilistic Machine Learning
• Authors: Raghul Parthipan, Hannah M. Christensen, J. Scott Hosking, Damon J. Wischik
• Abstract summary: We show that by using a physically-informed recurrent neural network within a probabilistic framework, our model for the 96 atmospheric simulation is competitive. This is due to a superior ability to model temporal correlations compared to standard first-order autoregressive schemes. We evaluate across a number of metrics from the literature, but also discuss how the probabilistic metric of likelihood may be a unifying choice for future climate models.
• Score: 1.5293427903448025
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The modelling of small-scale processes is a major source of error in climate models, hindering the accuracy of low-cost models which must approximate such processes through parameterization. Using stochasticity and machine learning have led to better models but there is a lack of work on combining the benefits from both. We show that by using a physically-informed recurrent neural network within a probabilistic framework, our resulting model for the Lorenz 96 atmospheric simulation is competitive and often superior to both a bespoke baseline and an existing probabilistic machine-learning (GAN) one. This is due to a superior ability to model temporal correlations compared to standard first-order autoregressive schemes. The model also generalises to unseen regimes. We evaluate across a number of metrics from the literature, but also discuss how the probabilistic metric of likelihood may be a unifying choice for future probabilistic climate models.

Related papers

• Supervised Score-Based Modeling by Gradient Boosting [49.556736252628745]
  We propose a Supervised Score-based Model (SSM) which can be viewed as a gradient boosting algorithm combining score matching. We provide a theoretical analysis of learning and sampling for SSM to balance inference time and prediction accuracy. Our model outperforms existing models in both accuracy and inference time.
  arXiv  Detail & Related papers  (2024-11-02T07:06:53Z)
• On the Efficient Marginalization of Probabilistic Sequence Models [3.5897534810405403]
  This dissertation focuses on using autoregressive models to answer complex probabilistic queries. We develop a class of novel and efficient approximation techniques for marginalization in sequential models that are model-agnostic.
  arXiv  Detail & Related papers  (2024-03-06T19:29:08Z)
• Neural parameter calibration for large-scale multi-agent models [0.7734726150561089]
  We present a method to retrieve accurate probability densities for parameters using neural equations. The two combined create a powerful tool that can quickly estimate densities on model parameters, even for very large systems.
  arXiv  Detail & Related papers  (2022-09-27T17:36:26Z)
• On the Influence of Enforcing Model Identifiability on Learning dynamics of Gaussian Mixture Models [14.759688428864159]
  We propose a technique for extracting submodels from singular models. Our method enforces model identifiability during training. We show how the method can be applied to more complex models like deep neural networks.
  arXiv  Detail & Related papers  (2022-06-17T07:50:22Z)
• Model-based micro-data reinforcement learning: what are the crucial model properties and which model to choose? [0.2836066255205732]
  We contribute to micro-data model-based reinforcement learning (MBRL) by rigorously comparing popular generative models. We find that on an environment that requires multimodal posterior predictives, mixture density nets outperform all other models by a large margin. We also found that deterministic models are on par, in fact they consistently (although non-significantly) outperform their probabilistic counterparts.
  arXiv  Detail & Related papers  (2021-07-24T11:38:25Z)
• MINIMALIST: Mutual INformatIon Maximization for Amortized Likelihood Inference from Sampled Trajectories [61.3299263929289]
  Simulation-based inference enables learning the parameters of a model even when its likelihood cannot be computed in practice. One class of methods uses data simulated with different parameters to infer an amortized estimator for the likelihood-to-evidence ratio. We show that this approach can be formulated in terms of mutual information between model parameters and simulated data.
  arXiv  Detail & Related papers  (2021-06-03T12:59:16Z)
• Anomaly Detection of Time Series with Smoothness-Inducing Sequential Variational Auto-Encoder [59.69303945834122]
  We present a Smoothness-Inducing Sequential Variational Auto-Encoder (SISVAE) model for robust estimation and anomaly detection of time series. Our model parameterizes mean and variance for each time-stamp with flexible neural networks. We show the effectiveness of our model on both synthetic datasets and public real-world benchmarks.
  arXiv  Detail & Related papers  (2021-02-02T06:15:15Z)
• Generative Temporal Difference Learning for Infinite-Horizon Prediction [101.59882753763888]
  We introduce the $gamma$-model, a predictive model of environment dynamics with an infinite probabilistic horizon. We discuss how its training reflects an inescapable tradeoff between training-time and testing-time compounding errors.
  arXiv  Detail & Related papers  (2020-10-27T17:54:12Z)
• Improving the Reconstruction of Disentangled Representation Learners via Multi-Stage Modeling [54.94763543386523]
  Current autoencoder-based disentangled representation learning methods achieve disentanglement by penalizing the ( aggregate) posterior to encourage statistical independence of the latent factors. We present a novel multi-stage modeling approach where the disentangled factors are first learned using a penalty-based disentangled representation learning method. Then, the low-quality reconstruction is improved with another deep generative model that is trained to model the missing correlated latent variables.
  arXiv  Detail & Related papers  (2020-10-25T18:51:15Z)
• Amortized Bayesian model comparison with evidential deep learning [0.12314765641075436]
  We propose a novel method for performing Bayesian model comparison using specialized deep learning architectures. Our method is purely simulation-based and circumvents the step of explicitly fitting all alternative models under consideration to each observed dataset. We show that our method achieves excellent results in terms of accuracy, calibration, and efficiency across the examples considered in this work.
  arXiv  Detail & Related papers  (2020-04-22T15:15:46Z)
• Hybrid modeling: Applications in real-time diagnosis [64.5040763067757]
  We outline a novel hybrid modeling approach that combines machine learning inspired models and physics-based models. We are using such models for real-time diagnosis applications.
  arXiv  Detail & Related papers  (2020-03-04T00:44:57Z)

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.