Gradient-free optimization of chaotic acoustics with reservoir computing

• URL: http://arxiv.org/abs/2106.09780v1
• Date: Thu, 17 Jun 2021 19:49:45 GMT
• Title: Gradient-free optimization of chaotic acoustics with reservoir computing
• Authors: Francisco Huhn and Luca Magri
• Abstract summary: We develop a versatile optimization method, which finds the design parameters that minimize time-averaged acoustic cost functionals. The method is gradient-free, model-informed, and data-driven with reservoir computing based on echo state networks.
• Score: 6.345523830122166
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We develop a versatile optimization method, which finds the design parameters that minimize time-averaged acoustic cost functionals. The method is gradient-free, model-informed, and data-driven with reservoir computing based on echo state networks. First, we analyse the predictive capabilities of echo state networks both in the short- and long-time prediction of the dynamics. We find that both fully data-driven and model-informed architectures learn the chaotic acoustic dynamics, both time-accurately and statistically. Informing the training with a physical reduced-order model with one acoustic mode markedly improves the accuracy and robustness of the echo state networks, whilst keeping the computational cost low. Echo state networks offer accurate predictions of the long-time dynamics, which would be otherwise expensive by integrating the governing equations to evaluate the time-averaged quantity to optimize. Second, we couple echo state networks with a Bayesian technique to explore the design thermoacoustic parameter space. The computational method is minimally intrusive. Third, we find the set of flame parameters that minimize the time-averaged acoustic energy of chaotic oscillations, which are caused by the positive feedback with a heat source, such as a flame in gas turbines or rocket motors. These oscillations are known as thermoacoustic oscillations. The optimal set of flame parameters is found with the same accuracy as brute-force grid search, but with a convergence rate that is more than one order of magnitude faster. This work opens up new possibilities for non-intrusive (``hands-off'') optimization of chaotic systems, in which the cost of generating data, for example from high-fidelity simulations and experiments, is high.

Related papers

• Expand and Compress: Exploring Tuning Principles for Continual Spatio-Temporal Graph Forecasting [17.530885640317372]
  We propose a novel prompt tuning-based continuous forecasting method. Specifically, we integrate the base-temporal graph neural network with a continuous prompt pool stored in memory. This method ensures that the model sequentially learns from the widespread-temporal data stream to accomplish tasks for corresponding periods.
  arXiv  Detail & Related papers  (2024-10-16T14:12:11Z)
• Modeling Time-Variant Responses of Optical Compressors with Selective State Space Models [0.0]
  This paper presents a method for modeling optical dynamic range compressors using deep neural networks with Selective State Space models. It features a refined technique integrating Feature-wise Linear Modulation and Gated Linear Units to adjust the network dynamically. The proposed architecture is well-suited for low-latency and real-time applications, crucial in live audio processing.
  arXiv  Detail & Related papers  (2024-08-22T17:03:08Z)
• 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)
• Efficient Numerical Wave Propagation Enhanced By An End-to-End Deep Learning Model [0.0]
  We present a novel unified system that integrates a numerical solver with a deep learning component into an end-to-end framework. A stable and fast solver further allows the use of Parareal, a parallel-in-time algorithm to correct high-frequency wave components.
  arXiv  Detail & Related papers  (2024-02-04T00:07:05Z)
• Boosting Fast and High-Quality Speech Synthesis with Linear Diffusion [85.54515118077825]
  This paper proposes a linear diffusion model (LinDiff) based on an ordinary differential equation to simultaneously reach fast inference and high sample quality. To reduce computational complexity, LinDiff employs a patch-based processing approach that partitions the input signal into small patches. Our model can synthesize speech of a quality comparable to that of autoregressive models with faster synthesis speed.
  arXiv  Detail & Related papers  (2023-06-09T07:02:43Z)
• Capturing dynamical correlations using implicit neural representations [85.66456606776552]
  We develop an artificial intelligence framework which combines a neural network trained to mimic simulated data from a model Hamiltonian with automatic differentiation to recover unknown parameters from experimental data. In doing so, we illustrate the ability to build and train a differentiable model only once, which then can be applied in real-time to multi-dimensional scattering data.
  arXiv  Detail & Related papers  (2023-04-08T07:55:36Z)
• An advanced spatio-temporal convolutional recurrent neural network for storm surge predictions [73.4962254843935]
  We study the capability of artificial neural network models to emulate storm surge based on the storm track/size/intensity history. This study presents a neural network model that can predict storm surge, informed by a database of synthetic storm simulations.
  arXiv  Detail & Related papers  (2022-04-18T23:42:18Z)
• Deep Impulse Responses: Estimating and Parameterizing Filters with Deep Networks [76.830358429947]
  Impulse response estimation in high noise and in-the-wild settings is a challenging problem. We propose a novel framework for parameterizing and estimating impulse responses based on recent advances in neural representation learning.
  arXiv  Detail & Related papers  (2022-02-07T18:57:23Z)
• Seismic wave propagation and inversion with Neural Operators [7.296366040398878]
  We develop a prototype framework for learning general solutions using a recently developed machine learning paradigm called Neural Operator. A trained Neural Operator can compute a solution in negligible time for any velocity structure or source location. We illustrate the method with the 2D acoustic wave equation and demonstrate the method's applicability to seismic tomography.
  arXiv  Detail & Related papers  (2021-08-11T19:17:39Z)
• Real-time parameter inference in reduced-order flame models with heteroscedastic Bayesian neural network ensembles [1.7188280334580197]
  We train our networks on a library of 2.1 million simulated flame videos. The trained neural networks are then used to infer model parameters from real videos of a premixed Bunsen flame.
  arXiv  Detail & Related papers  (2020-10-11T15:04:34Z)
• Temporal-Spatial Neural Filter: Direction Informed End-to-End Multi-channel Target Speech Separation [66.46123655365113]
  Target speech separation refers to extracting the target speaker's speech from mixed signals. Two main challenges are the complex acoustic environment and the real-time processing requirement. We propose a temporal-spatial neural filter, which directly estimates the target speech waveform from multi-speaker mixture.
  arXiv  Detail & Related papers  (2020-01-02T11:12:50Z)

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.