Flow based features and validation metric for machine learning reconstruction of PIV data

• URL: http://arxiv.org/abs/2105.13429v1
• Date: Thu, 27 May 2021 20:05:41 GMT
• Title: Flow based features and validation metric for machine learning reconstruction of PIV data
• Authors: Ghasem Akbari, Nader Montazerin
• Abstract summary: Reconstruction of flow field from real data by a physics-oriented approach is a current challenge for fluid scientists in the AI community. The present article applies machine learning approach to study contribution of different flow-based features. A metric is proposed that reflects mass conservation law as an important requirement for a physical flow reproduction.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Reconstruction of flow field from real sparse data by a physics-oriented approach is a current challenge for fluid scientists in the AI community. The problem includes feature recognition and implementation of AI algorithms that link data to a physical feature space in order to produce reconstructed data. The present article applies machine learning approach to study contribution of different flow-based features with practical fluid mechanics applications for reconstruction of the missing data of turbomachinery PIV measurements. Support vector regression (SVR) and multi-layer perceptron (MLP) are selected as two robust regressors capable of modelling non-linear fluid flow phenomena. The proposed flow-based features are optimally scaled and filtered to extract the best configuration. In addition to conventional data-based validation of the regressors, a metric is proposed that reflects mass conservation law as an important requirement for a physical flow reproduction. For a velocity field including 25% of clustered missing data, the reconstruction accuracy achieved by SVR in terms of R2-score is as high as 0.993 for the in-plane velocity vectors in comparison with that obtained by MLP which is up to 0.981. In terms of mass conservation metric, the SVR model by R2-score up to 0.96 is considerably more accurate than the MLP estimator. For extremely sparse data with a gappiness of 75%, vector and contour plots from SVR and MLP were consistent with those of the original field.

Related papers

• KFD-NeRF: Rethinking Dynamic NeRF with Kalman Filter [49.85369344101118]
  We introduce KFD-NeRF, a novel dynamic neural radiance field integrated with an efficient and high-quality motion reconstruction framework based on Kalman filtering. Our key idea is to model the dynamic radiance field as a dynamic system whose temporally varying states are estimated based on two sources of knowledge: observations and predictions. Our KFD-NeRF demonstrates similar or even superior performance within comparable computational time and state-of-the-art view synthesis performance with thorough training.
  arXiv  Detail & Related papers  (2024-07-18T05:48:24Z)
• A Multi-Grained Symmetric Differential Equation Model for Learning Protein-Ligand Binding Dynamics [74.93549765488103]
  In drug discovery, molecular dynamics simulation provides a powerful tool for predicting binding affinities, estimating transport properties, and exploring pocket sites. We propose NeuralMD, the first machine learning surrogate that can facilitate numerical MD and provide accurate simulations in protein-ligand binding. We show the efficiency and effectiveness of NeuralMD, with a 2000$times$ speedup over standard numerical MD simulation and outperforming all other ML approaches by up to 80% under the stability metric.
  arXiv  Detail & Related papers  (2024-01-26T09:35:17Z)
• A machine learning approach to the prediction of heat-transfer coefficients in micro-channels [4.724825031148412]
  The accurate prediction of the two-phase heat transfer coefficient (HTC) is key to the optimal design and operation of compact heat exchangers. We use a multi-output Gaussian process regression (GPR) to estimate the HTC in microchannels as a function of the mass flow rate, heat flux, system pressure and channel diameter and length.
  arXiv  Detail & Related papers  (2023-05-28T15:48:01Z)
• Optimization of a Hydrodynamic Computational Reservoir through Evolution [58.720142291102135]
  We interface with a model of a hydrodynamic system, under development by a startup, as a computational reservoir. We optimized the readout times and how inputs are mapped to the wave amplitude or frequency using an evolutionary search algorithm. Applying evolutionary methods to this reservoir system substantially improved separability on an XNOR task, in comparison to implementations with hand-selected parameters.
  arXiv  Detail & Related papers  (2023-04-20T19:15:02Z)
• RAMP-Net: A Robust Adaptive MPC for Quadrotors via Physics-informed Neural Network [6.309365332210523]
  We propose a Robust Adaptive MPC framework via PINNs (RAMP-Net), which uses a neural network trained partly from simple ODEs and partly from data. We report 7.8% to 43.2% and 8.04% to 61.5% reduction in tracking errors for speeds ranging from 0.5 to 1.75 m/s compared to two SOTA regression based MPC methods.
  arXiv  Detail & Related papers  (2022-09-19T16:11:51Z)
• Machine Learning model for gas-liquid interface reconstruction in CFD numerical simulations [59.84561168501493]
  The volume of fluid (VoF) method is widely used in multi-phase flow simulations to track and locate the interface between two immiscible fluids. A major bottleneck of the VoF method is the interface reconstruction step due to its high computational cost and low accuracy on unstructured grids. We propose a machine learning enhanced VoF method based on Graph Neural Networks (GNN) to accelerate the interface reconstruction on general unstructured meshes.
  arXiv  Detail & Related papers  (2022-07-12T17:07:46Z)
• Physics-informed machine learning with differentiable programming for heterogeneous underground reservoir pressure management [64.17887333976593]
  Avoiding over-pressurization in subsurface reservoirs is critical for applications like CO2 sequestration and wastewater injection. Managing the pressures by controlling injection/extraction are challenging because of complex heterogeneity in the subsurface. We use differentiable programming with a full-physics model and machine learning to determine the fluid extraction rates that prevent over-pressurization.
  arXiv  Detail & Related papers  (2022-06-21T20:38:13Z)
• SreaMRAK a Streaming Multi-Resolution Adaptive Kernel Algorithm [60.61943386819384]
  Existing implementations of KRR require that all the data is stored in the main memory. We propose StreaMRAK - a streaming version of KRR. We present a showcase study on two synthetic problems and the prediction of the trajectory of a double pendulum.
  arXiv  Detail & Related papers  (2021-08-23T21:03:09Z)
• Machine Learning based CVD Virtual Metrology in Mass Produced Semiconductor Process [0.0]
  A cross-benchmark has been done on three critical aspects, data imputing, feature selection and regression algorithms. The result reveals that linear feature selection regression algorithm would extensively under-fit the VM data. Data imputing is also necessary to achieve a higher prediction accuracy as the data availability is only 70% when optimal accuracy is obtained.
  arXiv  Detail & Related papers  (2021-07-11T15:32:31Z)
• Empirical Models for Multidimensional Regression of Fission Systems [0.0]
  We develop guidelines for developing empirical models for multidimensional regression of neutron transport. An assessment of the accuracy and precision finds that the SVR, followed closely by ANN, performs the best.
  arXiv  Detail & Related papers  (2021-05-30T22:53:39Z)
• Physics-aware deep neural networks for surrogate modeling of turbulent natural convection [0.0]
  We investigate the use of PINNs surrogate modeling for turbulent Rayleigh-B'enard convection flows. We show how it comes to play as a regularization close to the training boundaries which are zones of poor accuracy for standard PINNs. The predictive accuracy of the surrogate over the entire half a billion DNS coordinates yields errors for all flow variables ranging between [0.3% -- 4%] in the relative L 2 norm.
  arXiv  Detail & Related papers  (2021-03-05T09:48:57Z)

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.