Bayesian method refines surrogate models for accurate full waveform inversion.
problem Complex input/output relations in full waveform inversion make accurate surrogate models difficult.
method Iterative refinement of surrogate models using MCMC samples and progressively expanding frequency bandwidth.
result Highly accurate surrogate model across full bandwidth enables accurate final MCMC inversion.
Physics-consistent method improves seismic inversion accuracy.
problem Challenges in seismic full-waveform inversion (FWI) due to ill-posedness and high cost.
method Hybrid approach combining physics-based models with data-driven methodologies, incorporating physics into data augmentation.
result Physics-consistent data-driven inversion yields higher accuracy and better generalization.
We make posterior sampling in FWI feasible for large surveys.
problem Uncertainty-aware subsurface models at field scale.
method Coupling diffusion-based posterior sampling with simultaneous-source FWI data.
result Lower model error and better data fit at reduced computational cost.
Dual-space sampling tackles ill-conditioned inverse problems with Bayesian methods.
problem Bayesian inference in constrained inverse problems with ill-conditioned solutions.
method Dual-space posterior sampling using ADMM and SVGD.
result Well-calibrated uncertainty estimates and posterior contraction with increasing data.
This study improves uncertainty estimation in FWI using SVGD with annealing and PCA.
problem Uncertainty quantification in FWI solutions.
method Annealed Stein Variational Gradient Descent (SVGD) combined with multi-scale strategy and PCA.
result Improved uncertainty estimation in FWI solutions.
Stochastic optimization is key to efficient inversion in PDE-constrained optimization. Using 'simultaneous shots', or random superposition of source terms, works very well in simple acquisition geometries where all sources see all receivers, but this rarely occurs in practice. We develop an approach that interpolates d…
We present an application of deep generative models in the context of partial-differential equation (PDE) constrained inverse problems. We combine a generative adversarial network (GAN) representing an a priori model that creates subsurface geological structures and their petrophysical properties, with the numerical so…
Bayesian Neural Networks detect gravitational wave events with high accuracy and real-time potential.
problem Detecting and identifying the full duration of compact binary coalescence events in gravitational wave data.
method Integrating Bayesian approach into a CLDNN classifier that combines CNN and LSTM for event detection and uncertainty estimation.
result Successfully detected all seven BBH events in LIGO Livingston O2 data with high accuracy.
Study uses machine learning to solve photoacoustic tomography's inverse problem.
problem Solving the full inverse problem in photoacoustic tomography.
method Developed an approach using variational autoencoders for Bayesian estimation of the posterior distribution.
result Evaluated the approach with numerical simulations and compared it to a Bayesian solution.
Deep models memorize training data in geophysical inversion, leading to biased posterior distributions.
problem Memorization of training data biases learned priors in geophysical inverse problems.
method Casting generative models' training as maximum likelihood, we show memorization results in a reweighted empirical distribution for diffusion models, leading to Gaussian mixture priors and posteriors.
result Memorization leads to posterior distributions that are likelihood-weighted lookup among stored training examples, affecting full waveform inversion outcomes.
SAGE generates subsurface velocity models from sparse well logs and seismic images.
problem Lack of high-quality subsurface velocity models due to limited data availability.
method Subsurface AI-driven geostatistical extraction using proxy posterior.
result SAGE produces geologically plausible and statistically accurate velocity realizations.
Neural waveform models such as WaveNet have demonstrated better performance than conventional vocoders for statistical parametric speech synthesis. As an autoregressive (AR) model, WaveNet is limited by a slow sequential waveform generation process. Some new models that use the inverse-autoregressive flow (IAF) can gen…
Bayesian framework learns prior from data to quantify uncertainty in MRI reconstruction.
problem Quantifying uncertainty in deep learning solutions for inverse problems.
method Adopting denoising score matching to learn prior from data, using it in an annealed Hamiltonian Monte-Carlo scheme.
result The approach yields high-quality reconstructions and assesses uncertainty on specific features.
New method solves high-dimensional Bayesian inverse problems efficiently.
problem Efficiently solving high-dimensional Bayesian inverse problems with limited data.
method Physics-informed Neural Operators with RealNVP architecture for invertibility and differentiability.
result Accurate approximations of the full posterior without additional forward solves or sampling.
This paper proposes a new method to quantify uncertainty in reservoir characterization using invertible neural networks.
problem Quantifying uncertainty in reservoir characterization models.
method Training an invertible neural network to represent the posterior distribution of model parameters.
result The proposed method provides a more efficient and direct way to sample from the posterior distribution.
In this paper, a generalized multivariate Student-t mixture model is developed for classification and clustering of Low Probability of Intercept radar waveforms. A Low Probability of Intercept radar signal is characterized by a pulse compression waveform which is either frequency-modulated or phase-modulated. The propo…
iSTFTNet speeds up mel-spectrogram vocoders without sacrificing quality.
problem Efficiently converting mel-spectrograms to speech with minimal computation.
method Replaces convolutional layers with iSTFT after frequency dimension reduction.
result Significant reduction in computational cost with comparable quality.
Given discrete time observations over a fixed time interval, we study a nonparametric Bayesian approach to estimation of the volatility coefficient of a stochastic differential equation. We postulate a histogram-type prior on the volatility with piecewise constant realisations on bins forming a partition of the time in…
SBI improves uncertainty analysis of cardiovascular biomarkers.
problem Mapping waveforms back to plausible physiological parameters.
method Simulation-based inference (SBI) for statistical inference.
result Posterior distributions provide a multi-dimensional representation of uncertainty.
GABI learns geometry from diverse systems to improve Bayesian inference.
problem Bayesian inversion of physical systems with varying geometries.
method Geometric Autoencoders for Bayesian Inversion (GABI) learns geometry-aware priors from large datasets.
result GABI yields comparable predictive accuracy to deterministic methods and well-calibrated uncertainty quantification.
Machine learning classifies gravitational wave signals to test General Relativity.
problem Testing General Relativity with gravitational wave signals from binary black hole mergers.
method Convolutional Neural Networks (CNNs) trained on whitened waveforms and response function type observables.
result CNNs improve classification sensitivity by a factor of approximately 33 compared to whitened waveforms.
New method reduces memory usage for Bayesian inverse problems on large grids.
problem Solving large-scale linear inverse problems with Gaussian process priors.
method Implicit representation of posterior covariance matrices, sequential disintegrations of Gaussian measures.
result Significant reduction in uncertainty for high-density regions estimation.
Weak diffusion priors can still perform well in inverse problems.
problem Using mismatched or low-fidelity diffusion priors in inverse problems.
method Extensive experiments and theoretical analysis combining Bayesian-consistency theory and local-correlation analysis.
result Weak priors succeed when measurements are highly informative, and they fail in other regimes.
This paper proposes a new loss using short-time Fourier transform (STFT) spectra for the aim of training a high-performance neural speech waveform model that predicts raw continuous speech waveform samples directly. Not only amplitude spectra but also phase spectra obtained from generated speech waveforms are used to c…
Deep learning speeds up real-time emission monitoring.
problem Real-time greenhouse gas emission monitoring under transient conditions.
method Bayesian inference with deep learning surrogate of CFD outputs.
result Near-real-time predictions with orders-of-magnitude faster runtimes.
This paper tackles real-time Bayesian inverse problems using neural networks.
problem Real-time inference of posterior distributions from experimental data.
method Amortized variational inference with Gaussian and Flow guides.
result The approach provides posterior estimates in real-time at the cost of a forward pass.
We consider the problem of learning by demonstration from agents acting in unknown stochastic Markov environments or games. Our aim is to estimate agent preferences in order to construct improved policies for the same task that the agents are trying to solve. To do so, we extend previous probabilistic approaches for in…
We consider the problem of learning by demonstration from agents acting in unknown stochastic Markov environments or games. Our aim is to estimate agent preferences in order to construct improved policies for the same task that the agents are trying to solve. To do so, we extend previous probabilistic approaches for in…
DiffWave generates high-fidelity audio waveforms efficiently.
problem Conditional and unconditional audio waveform generation.
method Non-autoregressive diffusion model using Markov chain synthesis.
result DiffWave produces high-quality audios in various tasks.
DYMAG uses dynamic waveforms to improve graph neural networks.
problem Improving graph neural networks for better graph understanding.
method DYMAG employs dynamical system-based waveforms for message aggregation in graph neural networks.
result DYMAG outperforms baseline models in graph recovery, property prediction, and random graph generation.
This study proposes a fully convolutional network (FCN) model for raw waveform-based speech enhancement. The proposed system performs speech enhancement in an end-to-end (i.e., waveform-in and waveform-out) manner, which dif-fers from most existing denoising methods that process the magnitude spectrum (e.g., log power …
Bayesian Deep Learning tackles inverse problems with neural networks and approximate computations.
problem Solving inverse problems with indirect measurements and uncertainties.
method Bayesian Deep Learning, using neural networks and approximate computations.
result Effective solutions for inverse problems using Bayesian Deep Learning.
The methodology developed in this article is motivated by a wide range of prediction and uncertainty quantification problems that arise in Statistics, Machine Learning and Applied Mathematics, such as non-parametric regression, multi-class classification and inversion of partial differential equations. One popular form…
Unified framework for Bayesian PDE-constrained inversion using physics-informed neural networks.
problem Incorporating prior distributions in function space into Bayesian PINN-based inversion.
method Functional-prior-based approaches (fpBPINN) to Bayesian PDE-constrained inversion using physics-informed neural networks (PINNs). Two complementary approaches: FPI-BPINN and fParVI-PINN.
result Accurate estimation of posterior distributions in seismic traveltime tomography and Darcy-flow permeability inversion.
We propose a learning-based filter that allows us to directly modify a synthetic speech waveform into a natural speech waveform. Speech-processing systems using a vocoder framework such as statistical parametric speech synthesis and voice conversion are convenient especially for a limited number of data because it is p…
Bayesian geoacoustic inversion improved using MDN.
problem Efficiently solving Bayesian geoacoustic inversion problems.
method Deriving geoacoustic statistics from multidimensional posterior density using MDN, training the network on the whole parameter space.
result The network provides reliable predictions and good generalization performance, solving problems in seconds.
WaveCycleGAN has recently been proposed to bridge the gap between natural and synthesized speech waveforms in statistical parametric speech synthesis and provides fast inference with a moving average model rather than an autoregressive model and high-quality speech synthesis with the adversarial training. However, the …
Develops a universal waveform selection scheme for radar tracking.
problem Optimal waveform selection for target tracking in active sensors.
method Uses reinforcement learning and universal source coding techniques.
result Achieves optimal waveform selection for any radar scene modeled as a Markov process.
Neural waveform models such as the WaveNet are used in many recent text-to-speech systems, but the original WaveNet is quite slow in waveform generation because of its autoregressive (AR) structure. Although faster non-AR models were recently reported, they may be prohibitively complicated due to the use of a distillin…
Dual PC algorithm improves structure learning of Bayesian networks.
problem Learning the structure of Bayesian networks from observational data.
method Dual PC algorithm, leveraging covariance and precision matrices, and partial correlations.
result The dual PC algorithm outperforms the classic PC algorithm in structure recovery, even with non-Gaussian data.
We propose a predictive neural network architecture that can be utilized to update reference velocity models as inputs to the full waveform inversion. Deep learning models are explored to augment velocity model building workflows during processing the 3D seismic volume in salt-prone environments. Specifically, a neural…
VPFD uses vocoder features for adversarial training in VC.
problem Adversarial training on waveform data is time-consuming and memory-intensive.
method VPFD employs vocoder features for adversarial training.
result VPFD achieves VC performance comparable to waveform discriminators with reduced training time and memory.
Real-time speech enhancement model removes various noises and reverb.
problem Real-time speech enhancement in noisy environments.
method Causal speech enhancement model using encoder-decoder architecture with skip-connections, optimized in time and frequency domains.
result The model matches state-of-the-art performance while working directly on raw waveform.
Recent speech technology research has seen a growing interest in using WaveNets as statistical vocoders, i.e., generating speech waveforms from acoustic features. These models have been shown to improve the generated speech quality over classical vocoders in many tasks, such as text-to-speech synthesis and voice conver…
New analysis shows scores learn data manifolds better than distributions.
problem Learning the full distribution vs. just the data manifold.
method Novel analysis of scores in the small-σ regime.
result Scores learn data manifold information Θ(σ−2) stronger than distribution information. Discrete return (DR) Laser Detection and Ranging (Ladar) systems provide a series of echoes that reflect from objects in a scene. These can be first, last or multi-echo returns. In contrast, Full-Waveform (FW)-Ladar systems measure the intensity of light reflected from objects continuously over a period of time. In a c…
Novel method uses deep generative models for efficient Bayesian inverse problem solving.
problem Efficiently solving inverse problems with large, discrete fields and limited prior information.
method Bayesian inference with deep generative models in low-dimensional latent space.
result Accurate and reliable uncertainty estimates for large-scale inverse problems.
Bayesian inverse problems solved with Gaussian models for PDEs.
problem Solving inverse problems with limited data for PDEs.
method Constructing PDE-informed Gaussian priors for Bayesian inversion.
result PDE-informed Gaussian priors outperform traditional priors.