Generalization capabilities of conditional GAN for turbulent flow under changes of geometry

• URL: http://arxiv.org/abs/2302.09945v1
• Date: Mon, 20 Feb 2023 12:21:34 GMT
• Title: Generalization capabilities of conditional GAN for turbulent flow under changes of geometry
• Authors: Claudia Drygala, Francesca di Mare, Hanno Gottschalk
• Abstract summary: generative adversarial networks (GAN) for the synthetic modeling of turbulence is a mathematically well-founded approach to overcome this issue. In this work, we investigate the generalization capabilites of GAN-based synthetic turbulence generators when geometrical changes occur in the flow configuration. We show the abilities and limits of generalization for the conditional GAN by extending the regions of the extracted wake positions.
• Score: 0.6445605125467573
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Turbulent flow consists of structures with a wide range of spatial and temporal scales which are hard to resolve numerically. Classical numerical methods as the Large Eddy Simulation (LES) are able to capture fine details of turbulent structures but come at high computational cost. Applying generative adversarial networks (GAN) for the synthetic modeling of turbulence is a mathematically well-founded approach to overcome this issue. In this work, we investigate the generalization capabilites of GAN-based synthetic turbulence generators when geometrical changes occur in the flow configuration (e.g. aerodynamic geometric optimization of structures such as airfoils). As training data, we use the flow around a low-pressure turbine (LPT) stator with periodic wake impact obtained from highly resolved LES. To simulate the flow around a LPT stator, we use the conditional deep convolutional GAN framework pix2pixHD conditioned on the position of a rotating wake in front of the stator. For the generalization experiments we exclude images of wake positions located at certain regions from the training data and use the unseen data for testing. We show the abilities and limits of generalization for the conditional GAN by extending the regions of the extracted wake positions successively. Finally, we evaluate the statistical properties of the synthesized flow field by comparison with the corresponding LES results.

Related papers

• Bayesian Conditional Diffusion Models for Versatile Spatiotemporal Turbulence Generation [13.278744447861289]
  We introduce a novel generative framework grounded in probabilistic diffusion models for turbulence generation. A notable feature of our approach is the proposed method for long-span flow sequence generation, which is based on autoregressive-based conditional sampling. We showcase the versatile turbulence generation capability of our framework through a suite of numerical experiments.
  arXiv  Detail & Related papers  (2023-11-14T04:08:14Z)
• GAFlow: Incorporating Gaussian Attention into Optical Flow [62.646389181507764]
  We push Gaussian Attention (GA) into the optical flow models to accentuate local properties during representation learning. We introduce a novel Gaussian-Constrained Layer (GCL) which can be easily plugged into existing Transformer blocks. For reliable motion analysis, we provide a new Gaussian-Guided Attention Module (GGAM)
  arXiv  Detail & Related papers  (2023-09-28T07:46:01Z)
• Manifold Interpolating Optimal-Transport Flows for Trajectory Inference [64.94020639760026]
  We present a method called Manifold Interpolating Optimal-Transport Flow (MIOFlow) MIOFlow learns, continuous population dynamics from static snapshot samples taken at sporadic timepoints. We evaluate our method on simulated data with bifurcations and merges, as well as scRNA-seq data from embryoid body differentiation, and acute myeloid leukemia treatment.
  arXiv  Detail & Related papers  (2022-06-29T22:19:03Z)
• Efficient CDF Approximations for Normalizing Flows [64.60846767084877]
  We build upon the diffeomorphic properties of normalizing flows to estimate the cumulative distribution function (CDF) over a closed region. Our experiments on popular flow architectures and UCI datasets show a marked improvement in sample efficiency as compared to traditional estimators.
  arXiv  Detail & Related papers  (2022-02-23T06:11:49Z)
• Generative Modeling of Turbulence [0.7646713951724012]
  We present a mathematically well founded approach for the synthetic modeling of turbulent flows using generative adversarial networks (GAN) GAN are efficient in simulating turbulence in technically challenging flow problems on the basis of a moderate amount of training date.
  arXiv  Detail & Related papers  (2021-12-05T11:39:14Z)
• Learning the structure of wind: A data-driven nonlocal turbulence model for the atmospheric boundary layer [0.0]
  We develop a novel data-driven approach to modeling the atmospheric boundary layer. This approach leads to a nonlocal, anisotropic synthetic turbulence model which we refer to as the deep rapid distortion (DRD) model.
  arXiv  Detail & Related papers  (2021-07-23T06:41:33Z)
• Data-driven Aerodynamic Analysis of Structures using Gaussian Processes [0.0]
  This paper presents a data-driven model of the nonlinear self-excited forces acting on bridges. The framework is applied to a streamlined and bluff bridge deck based on Computational Fluid Dynamics (CFD) data. Further applications of the presented framework are foreseen in the design and online real-time monitoring of slender line-like structures.
  arXiv  Detail & Related papers  (2021-03-20T11:22:24Z)
• Leveraging Global Parameters for Flow-based Neural Posterior Estimation [90.21090932619695]
  Inferring the parameters of a model based on experimental observations is central to the scientific method. A particularly challenging setting is when the model is strongly indeterminate, i.e., when distinct sets of parameters yield identical observations. We present a method for cracking such indeterminacy by exploiting additional information conveyed by an auxiliary set of observations sharing global parameters.
  arXiv  Detail & Related papers  (2021-02-12T12:23:13Z)
• Conditioning Trick for Training Stable GANs [70.15099665710336]
  We propose a conditioning trick, called difference departure from normality, applied on the generator network in response to instability issues during GAN training. We force the generator to get closer to the departure from normality function of real samples computed in the spectral domain of Schur decomposition.
  arXiv  Detail & Related papers  (2020-10-12T16:50:22Z)
• Automating Turbulence Modeling by Multi-Agent Reinforcement Learning [4.784658158364452]
  We introduce multi-agent reinforcement learning as an automated discovery tool of turbulence models. We demonstrate the potential of this approach on Large Eddy Simulations of homogeneous and isotropic turbulence.
  arXiv  Detail & Related papers  (2020-05-18T18:45:09Z)
• A Near-Optimal Gradient Flow for Learning Neural Energy-Based Models [93.24030378630175]
  We propose a novel numerical scheme to optimize the gradient flows for learning energy-based models (EBMs) We derive a second-order Wasserstein gradient flow of the global relative entropy from Fokker-Planck equation. Compared with existing schemes, Wasserstein gradient flow is a smoother and near-optimal numerical scheme to approximate real data densities.
  arXiv  Detail & Related papers  (2019-10-31T02:26:20Z)

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.