Applying Machine Learning to Study Fluid Mechanics

• URL: http://arxiv.org/abs/2110.02083v1
• Date: Tue, 5 Oct 2021 14:30:24 GMT
• Title: Applying Machine Learning to Study Fluid Mechanics
• Authors: Steven L. Brunton
• Abstract summary: This paper provides a short overview of how to use machine learning to build data-driven models in fluid mechanics. At each stage, we discuss how prior physical knowledge may be embedding into the process, with specific examples from the field of fluid mechanics.
• Score: 0.696194614504832
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper provides a short overview of how to use machine learning to build data-driven models in fluid mechanics. The process of machine learning is broken down into five stages: (1) formulating a problem to model, (2) collecting and curating training data to inform the model, (3) choosing an architecture with which to represent the model, (4) designing a loss function to assess the performance of the model, and (5) selecting and implementing an optimization algorithm to train the model. At each stage, we discuss how prior physical knowledge may be embedding into the process, with specific examples from the field of fluid mechanics.

Related papers

• Learning Low-Dimensional Strain Models of Soft Robots by Looking at the Evolution of Their Shape with Application to Model-Based Control [2.058941610795796]
  This paper introduces a streamlined method for learning low-dimensional, physics-based models. We validate our approach through simulations with various planar soft manipulators. Thanks to the capability of the method of generating physically compatible models, the learned models can be straightforwardly combined with model-based control policies.
  arXiv  Detail & Related papers  (2024-10-31T18:37:22Z)
• An Information Theoretic Approach to Machine Unlearning [45.600917449314444]
  Key challenge in unlearning is forgetting the necessary data in a timely manner, while preserving model performance. In this work, we address the zero-shot unlearning scenario, whereby an unlearning algorithm must be able to remove data given only a trained model and the data to be forgotten. We derive a simple but principled zero-shot unlearning method based on the geometry of the model.
  arXiv  Detail & Related papers  (2024-02-02T13:33:30Z)
• CodeGen2: Lessons for Training LLMs on Programming and Natural Languages [116.74407069443895]
  We unify encoder and decoder-based models into a single prefix-LM. For learning methods, we explore the claim of a "free lunch" hypothesis. For data distributions, the effect of a mixture distribution and multi-epoch training of programming and natural languages on model performance is explored.
  arXiv  Detail & Related papers  (2023-05-03T17:55:25Z)
• Advancing Reacting Flow Simulations with Data-Driven Models [50.9598607067535]
  Key to effective use of machine learning tools in multi-physics problems is to couple them to physical and computer models. The present chapter reviews some of the open opportunities for the application of data-driven reduced-order modeling of combustion systems.
  arXiv  Detail & Related papers  (2022-09-05T16:48:34Z)
• Model-agnostic multi-objective approach for the evolutionary discovery of mathematical models [55.41644538483948]
  In modern data science, it is more interesting to understand the properties of the model, which parts could be replaced to obtain better results. We use multi-objective evolutionary optimization for composite data-driven model learning to obtain the algorithm's desired properties.
  arXiv  Detail & Related papers  (2021-07-07T11:17:09Z)
• Interleaving Learning, with Application to Neural Architecture Search [12.317568257671427]
  We propose a novel machine learning framework referred to as interleaving learning (IL) In our framework, a set of models collaboratively learn a data encoder in an interleaving fashion. We apply interleaving learning to search neural architectures for image classification on CIFAR-10, CIFAR-100, and ImageNet.
  arXiv  Detail & Related papers  (2021-03-12T00:54:22Z)
• Sufficiently Accurate Model Learning for Planning [119.80502738709937]
  This paper introduces the constrained Sufficiently Accurate model learning approach. It provides examples of such problems, and presents a theorem on how close some approximate solutions can be. The approximate solution quality will depend on the function parameterization, loss and constraint function smoothness, and the number of samples in model learning.
  arXiv  Detail & Related papers  (2021-02-11T16:27:31Z)
• Choice modelling in the age of machine learning -- discussion paper [0.27998963147546135]
  Cross-pollination of machine learning models, techniques and practices could help overcome problems and limitations encountered in the current theory-driven paradigm. Despite the potential benefits of using the advances of machine learning to improve choice modelling practices, the choice modelling field has been hesitant to embrace machine learning.
  arXiv  Detail & Related papers  (2021-01-28T11:57:08Z)
• Distilling Interpretable Models into Human-Readable Code [71.11328360614479]
  Human-readability is an important and desirable standard for machine-learned model interpretability. We propose to train interpretable models using conventional methods, and then distill them into concise, human-readable code. We describe a piecewise-linear curve-fitting algorithm that produces high-quality results efficiently and reliably across a broad range of use cases.
  arXiv  Detail & Related papers  (2021-01-21T01:46:36Z)
• Structured learning of rigid-body dynamics: A survey and unified view from a robotics perspective [5.597839822252915]
  We study supervised regression models that combine rigid-body mechanics with data-driven modelling techniques. We provide a unified view on the combination of data-driven regression models, such as neural networks and Gaussian processes, with analytical model priors.
  arXiv  Detail & Related papers  (2020-12-11T11:26:48Z)
• Learning Predictive Representations for Deformable Objects Using Contrastive Estimation [83.16948429592621]
  We propose a new learning framework that jointly optimize both the visual representation model and the dynamics model. We show substantial improvements over standard model-based learning techniques across our rope and cloth manipulation suite.
  arXiv  Detail & Related papers  (2020-03-11T17:55:15Z)

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.