Combining data assimilation and machine learning to infer unresolved scale parametrisation

• URL: http://arxiv.org/abs/2009.04318v2
• Date: Tue, 8 Dec 2020 11:13:51 GMT
• Title: Combining data assimilation and machine learning to infer unresolved scale parametrisation
• Authors: Julien Brajard, Alberto Carrassi, Marc Bocquet and Laurent Bertino
• Abstract summary: In recent years, machine learning has been proposed to devise data-driven parametrisations of unresolved processes in dynamical numerical models. Our goal is to go beyond the use of high-resolution simulations and train ML-based parametrisation using direct data. We show that in both cases the hybrid model yields forecasts with better skill than the truncated model.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In recent years, machine learning (ML) has been proposed to devise data-driven parametrisations of unresolved processes in dynamical numerical models. In most cases, the ML training leverages high-resolution simulations to provide a dense, noiseless target state. Our goal is to go beyond the use of high-resolution simulations and train ML-based parametrisation using direct data, in the realistic scenario of noisy and sparse observations. The algorithm proposed in this work is a two-step process. First, data assimilation (DA) techniques are applied to estimate the full state of the system from a truncated model. The unresolved part of the truncated model is viewed as a model error in the DA system. In a second step, ML is used to emulate the unresolved part, a predictor of model error given the state of the system. Finally, the ML-based parametrisation model is added to the physical core truncated model to produce a hybrid model. The DA component of the proposed method relies on an ensemble Kalman filter while the ML parametrisation is represented by a neural network. The approach is applied to the two-scale Lorenz model and to MAOOAM, a reduced-order coupled ocean-atmosphere model. We show that in both cases the hybrid model yields forecasts with better skill than the truncated model. Moreover, the attractor of the system is significantly better represented by the hybrid model than by the truncated model.

Related papers

• SMILE: Zero-Shot Sparse Mixture of Low-Rank Experts Construction From Pre-Trained Foundation Models [85.67096251281191]
  We present an innovative approach to model fusion called zero-shot Sparse MIxture of Low-rank Experts (SMILE) construction. SMILE allows for the upscaling of source models into an MoE model without extra data or further training. We conduct extensive experiments across diverse scenarios, such as image classification and text generation tasks, using full fine-tuning and LoRA fine-tuning.
  arXiv  Detail & Related papers  (2024-08-19T17:32:15Z)
• EMR-Merging: Tuning-Free High-Performance Model Merging [55.03509900949149]
  We show that Elect, Mask & Rescale-Merging (EMR-Merging) shows outstanding performance compared to existing merging methods. EMR-Merging is tuning-free, thus requiring no data availability or any additional training while showing impressive performance.
  arXiv  Detail & Related papers  (2024-05-23T05:25:45Z)
• Scaling up machine learning-based chemical plant simulation: A method for fine-tuning a model to induce stable fixed points [2.0599237172837523]
  We use a structured approach to fit a Machine Learning model directly to plant sensor data. We find that for smaller plants, this approach works well, but for larger plants, the complex dynamics arising from large and nested cycles in the flowsheet lead to instabilities in the solver. We show that a high accuracy of the single-unit models is not enough: The gradient can point in unexpected directions, which prevents the solver from converging to the correct stationary state.
  arXiv  Detail & Related papers  (2023-07-25T16:23:32Z)
• Automatic Parameterization for Aerodynamic Shape Optimization via Deep Geometric Learning [60.69217130006758]
  We propose two deep learning models that fully automate shape parameterization for aerodynamic shape optimization. Both models are optimized to parameterize via deep geometric learning to embed human prior knowledge into learned geometric patterns. We perform shape optimization experiments on 2D airfoils and discuss the applicable scenarios for the two models.
  arXiv  Detail & Related papers  (2023-05-03T13:45:40Z)
• 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)
• Dynamically-Scaled Deep Canonical Correlation Analysis [77.34726150561087]
  Canonical Correlation Analysis (CCA) is a method for feature extraction of two views by finding maximally correlated linear projections of them. We introduce a novel dynamic scaling method for training an input-dependent canonical correlation model.
  arXiv  Detail & Related papers  (2022-03-23T12:52:49Z)
• Inverting brain grey matter models with likelihood-free inference: a tool for trustable cytoarchitecture measurements [62.997667081978825]
  characterisation of the brain grey matter cytoarchitecture with quantitative sensitivity to soma density and volume remains an unsolved challenge in dMRI. We propose a new forward model, specifically a new system of equations, requiring a few relatively sparse b-shells. We then apply modern tools from Bayesian analysis known as likelihood-free inference (LFI) to invert our proposed model.
  arXiv  Detail & Related papers  (2021-11-15T09:08:27Z)
• A Data-driven feature selection and machine-learning model benchmark for the prediction of longitudinal dispersion coefficient [29.58577229101903]
  An accurate prediction on Longitudinal Dispersion(LD) coefficient can produce a performance leap in related simulation. In this study, a global optimal feature set was proposed through numerical comparison of the distilled local optimums in performance with representative ML models. Results show that the support vector machine has significantly better performance than other models.
  arXiv  Detail & Related papers  (2021-07-16T09:50:38Z)
• Training Structured Mechanical Models by Minimizing Discrete Euler-Lagrange Residual [36.52097893036073]
  Structured Mechanical Models (SMMs) are a data-efficient black-box parameterization of mechanical systems. We propose a methodology for fitting SMMs to data by minimizing the discrete Euler-Lagrange residual. Experiments show that our methodology learns models that are better in accuracy to those of the conventional schemes for fitting SMMs.
  arXiv  Detail & Related papers  (2021-05-05T00:44:01Z)
• Scaling Hidden Markov Language Models [118.55908381553056]
  This work revisits the challenge of scaling HMMs to language modeling datasets. We propose methods for scaling HMMs to massive state spaces while maintaining efficient exact inference, a compact parameterization, and effective regularization.
  arXiv  Detail & Related papers  (2020-11-09T18:51:55Z)
• Using machine learning to correct model error in data assimilation and forecast applications [0.0]
  We propose to use this method to correct the error of an existent, knowledge-based model. The resulting surrogate model is an hybrid model between the original (knowledge-based) model and the ML model. Using the hybrid surrogate models for DA yields a significantly better analysis than using the original model.
  arXiv  Detail & Related papers  (2020-10-23T18:30:45Z)

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.