Simulating Anisoplanatic Turbulence by Sampling Inter-modal and Spatially Correlated Zernike Coefficients

• URL: http://arxiv.org/abs/2004.11210v2
• Date: Tue, 23 Jun 2020 03:03:08 GMT
• Title: Simulating Anisoplanatic Turbulence by Sampling Inter-modal and Spatially Correlated Zernike Coefficients
• Authors: Nicholas Chimitt and Stanley H. Chan
• Abstract summary: We present a propagation-free method for simulating imaging through turbulence. We propose a new method to draw inter-modal and spatially correlated Zernike coefficients. Experimental results show that the simulator has an excellent match with the theory and real turbulence data.
• Score: 15.904420927818201
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Simulating atmospheric turbulence is an essential task for evaluating turbulence mitigation algorithms and training learning-based methods. Advanced numerical simulators for atmospheric turbulence are available, but they require evaluating wave propagation which is computationally expensive. In this paper, we present a propagation-free method for simulating imaging through turbulence. The key idea behind our work is a new method to draw inter-modal and spatially correlated Zernike coefficients. By establishing the equivalence between the angle-of-arrival correlation by Basu, McCrae and Fiorino (2015) and the multi-aperture correlation by Chanan (1992), we show that the Zernike coefficients can be drawn according to a covariance matrix defining the correlations. We propose fast and scalable sampling strategies to draw these samples. The new method allows us to compress the wave propagation problem into a sampling problem, hence making the new simulator significantly faster than existing ones. Experimental results show that the simulator has an excellent match with the theory and real turbulence data.

Related papers

• Oscillation Inversion: Understand the structure of Large Flow Model through the Lens of Inversion Method [60.88467353578118]
  We show that a fixed-point-inspired iterative approach to invert real-world images does not achieve convergence, instead oscillating between distinct clusters. We introduce a simple and fast distribution transfer technique that facilitates image enhancement, stroke-based recoloring, as well as visual prompt-guided image editing.
  arXiv  Detail & Related papers  (2024-11-17T17:45:37Z)
• Ensemble-based, large-eddy reconstruction of wind turbine inflow in a near-stationary atmospheric boundary layer through generative artificial intelligence [0.0]
  We develop a technique for time-resolved inflow reconstruction rooted in a large-eddy simulation model of the atmosphere. Our "large-eddy reconstruction" technique blends observations and atmospheric model information through a diffusion model machine learning algorithm. We verify the second-by-second reconstruction capability of our technique in three synthetic field campaigns.
  arXiv  Detail & Related papers  (2024-10-17T20:53:04Z)
• Improving Consistency Models with Generator-Induced Flows [16.049476783301724]
  Consistency models imitate the multi-step sampling of score-based diffusion in a single forward pass of a neural network. They can be learned in two ways: consistency distillation and consistency training. We propose a novel flow that transports noisy data towards their corresponding outputs derived from the currently trained model.
  arXiv  Detail & Related papers  (2024-06-13T20:22:38Z)
• Adaptive Federated Learning Over the Air [108.62635460744109]
  We propose a federated version of adaptive gradient methods, particularly AdaGrad and Adam, within the framework of over-the-air model training. Our analysis shows that the AdaGrad-based training algorithm converges to a stationary point at the rate of $mathcalO( ln(T) / T 1 - frac1alpha ).
  arXiv  Detail & Related papers  (2024-03-11T09:10:37Z)
• DiffuSeq-v2: Bridging Discrete and Continuous Text Spaces for Accelerated Seq2Seq Diffusion Models [58.450152413700586]
  We introduce a soft absorbing state that facilitates the diffusion model in learning to reconstruct discrete mutations based on the underlying Gaussian space. We employ state-of-the-art ODE solvers within the continuous space to expedite the sampling process. Our proposed method effectively accelerates the training convergence by 4x and generates samples of similar quality 800x faster.
  arXiv  Detail & Related papers  (2023-10-09T15:29:10Z)
• Importance sampling for stochastic quantum simulations [68.8204255655161]
  We introduce the qDrift protocol, which builds random product formulas by sampling from the Hamiltonian according to the coefficients. We show that the simulation cost can be reduced while achieving the same accuracy, by considering the individual simulation cost during the sampling stage. Results are confirmed by numerical simulations performed on a lattice nuclear effective field theory.
  arXiv  Detail & Related papers  (2022-12-12T15:06:32Z)
• Fast Sampling of Diffusion Models via Operator Learning [74.37531458470086]
  We use neural operators, an efficient method to solve the probability flow differential equations, to accelerate the sampling process of diffusion models. Compared to other fast sampling methods that have a sequential nature, we are the first to propose a parallel decoding method. We show our method achieves state-of-the-art FID of 3.78 for CIFAR-10 and 7.83 for ImageNet-64 in the one-model-evaluation setting.
  arXiv  Detail & Related papers  (2022-11-24T07:30:27Z)
• A unified method of data assimilation and turbulence modeling for separated flows at high Reynolds numbers [0.0]
  In this paper, we propose an improved ensemble kalman inversion method as a unified approach of data assimilation and turbulence modeling. The trainable parameters of the DNN are optimized according to the given experimental surface pressure coefficients. The results show that through joint assimilation of vary few experimental states, we can get turbulence models generalizing well to both attached and separated flows.
  arXiv  Detail & Related papers  (2022-11-01T17:17:53Z)
• 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)
• Learned Turbulence Modelling with Differentiable Fluid Solvers [23.535052848123932]
  We train turbulence models based on convolutional neural networks. These models improve under-resolved low resolution solutions to the incompressible Navier-Stokes equations at simulation time.
  arXiv  Detail & Related papers  (2022-02-14T19:03:01Z)
• Lightning-Fast Gravitational Wave Parameter Inference through Neural Amortization [6.810835072367285]
  Latest advances in neural simulation-based inference can speed up the inference time by up to three orders of magnitude. We find that our model correctly estimates credible intervals for the parameters of simulated gravitational waves.
  arXiv  Detail & Related papers  (2020-10-24T16:48:24Z)

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.