An approximate graph elicits detonation lattice
- URL: http://arxiv.org/abs/2603.16524v1
- Date: Tue, 17 Mar 2026 13:46:29 GMT
- Title: An approximate graph elicits detonation lattice
- Abstract summary: The proposed training-free algorithm is designed to accurately extract cellular patterns from 3D pressure traces.<n>Although the framework is robust, it remains challenging to reliably segment and quantify highly complex cellular patterns.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: This study presents a novel algorithm based on graph theory for the precise segmentation and measurement of detonation cells from 3D pressure traces, termed detonation lattices, addressing the limitations of manual and primitive 2D edge detection methods prevalent in the field. Using a segmentation model, the proposed training-free algorithm is designed to accurately extract cellular patterns, a longstanding challenge in detonations research. First, the efficacy of segmentation on generated data is shown with a prediction error 2%. Next, 3D simulation data is used to establish performance of the graph-based workflow. The results of statistics and joint probability densities show oblong cells aligned with the wave propagation axis with 17% deviation, whereas larger dispersion in volume reflects cubic amplification of linear variability. Although the framework is robust, it remains challenging to reliably segment and quantify highly complex cellular patterns. However, the graph-based formulation generalizes across diverse cellular geometries, positioning it as a practical tool for detonation analysis and a strong foundation for future extensions in triple-point collision studies.
Related papers
- Deep Learning-Based Characterization of Detonation-Cell Size Distributions in Soot-Foil Records [2.238128434525916]
We propose a novel method for automated recognition and high-order feature extraction of detonation cells based on deep learning instance segmentation (Mask R-CNN)<n>Results indicate high pixel-level agreement in benchmark validations and strong robustness against noise in complex real-world soot foils.<n>The proposed method enhances the efficiency and objectivity of statistical analysis, providing a powerful data extraction tool for experimental and numerical soot foils.
arXiv Detail & Related papers (2026-07-04T08:32:39Z) - Diffusion Model for Manifold Data: Score Decomposition, Curvature, and Statistical Complexity [93.52679474816728]
Diffusion models have become a leading framework in generative modeling.<n>This paper investigates how diffusion models learn structured data, focusing on two key aspects: statistical complexity and influence of data geometric properties.<n>We also highlight the interplay of manifold curvature with the structures in the score function.
arXiv Detail & Related papers (2026-03-21T04:40:19Z) - Enabling Probabilistic Learning on Manifolds through Double Diffusion Maps [3.081704060720176]
We present a generative learning framework for probabilistic sampling based on an extension of the Probabilistic Learning on Manifolds (PLoM) approach.<n>We solve a full-order ISDE directly in the latent space, preserving the full dynamical complexity of the system.
arXiv Detail & Related papers (2025-06-02T20:58:49Z) - 3D Cell Oversegmentation Correction via Geo-Wasserstein Divergence [6.0532832719253]
Oversegmentation is when a single cell is incorrectly split into multiple fragments.<n>This degrades the final segmentation quality and is notoriously difficult to resolve.<n>We propose a geometric framework to identify and correct these errors.<n>We also introduce a novel metric, Geo-Wasserstein divergence, to quantify changes in 2D geometries.
arXiv Detail & Related papers (2025-02-03T23:47:45Z) - A Machine Learning Based Approach for Statistical Analysis of Detonation Cells from Soot Foils [0.0]
The proposed algorithm is designed to accurately extract cellular patterns without a training procedure or dataset.
The results demonstrated consistent accuracy, with errors remaining within 10%, even in complex cases.
This work highlights the broad applicability and potential of the algorithm to advance the understanding of detonation wave dynamics.
arXiv Detail & Related papers (2024-09-10T12:50:46Z) - Dynamical Regimes of Diffusion Models [14.797301819675454]
We study generative diffusion models in the regime where the dimension of space and the number of data are large.
Our analysis reveals three distinct dynamical regimes during the backward generative diffusion process.
The dependence of the collapse time on the dimension and number of data provides a thorough characterization of the curse of dimensionality for diffusion models.
arXiv Detail & Related papers (2024-02-28T17:19:26Z) - Data Augmentation for Supervised Graph Outlier Detection via Latent Diffusion Models [39.33024157496401]
We introduce GODM, a novel data augmentation for mitigating class imbalance in supervised graph outlier detection.
Extensive experiments conducted on multiple datasets substantiate the effectiveness and efficiency of GODM.
We encapsulate GODM into a plug-and-play package and release it at PyPI.
arXiv Detail & Related papers (2023-12-29T16:50:40Z) - Exploiting Diffusion Prior for Generalizable Dense Prediction [85.4563592053464]
Recent advanced Text-to-Image (T2I) diffusion models are sometimes too imaginative for existing off-the-shelf dense predictors to estimate.
We introduce DMP, a pipeline utilizing pre-trained T2I models as a prior for dense prediction tasks.
Despite limited-domain training data, the approach yields faithful estimations for arbitrary images, surpassing existing state-of-the-art algorithms.
arXiv Detail & Related papers (2023-11-30T18:59:44Z) - Graph Out-of-Distribution Generalization with Controllable Data
Augmentation [51.17476258673232]
Graph Neural Network (GNN) has demonstrated extraordinary performance in classifying graph properties.
Due to the selection bias of training and testing data, distribution deviation is widespread.
We propose OOD calibration to measure the distribution deviation of virtual samples.
arXiv Detail & Related papers (2023-08-16T13:10:27Z) - Score-based Diffusion Models in Function Space [137.70916238028306]
Diffusion models have recently emerged as a powerful framework for generative modeling.<n>This work introduces a mathematically rigorous framework called Denoising Diffusion Operators (DDOs) for training diffusion models in function space.<n>We show that the corresponding discretized algorithm generates accurate samples at a fixed cost independent of the data resolution.
arXiv Detail & Related papers (2023-02-14T23:50:53Z) - Graph Generation with Diffusion Mixture [57.78958552860948]
Generation of graphs is a major challenge for real-world tasks that require understanding the complex nature of their non-Euclidean structures.
We propose a generative framework that models the topology of graphs by explicitly learning the final graph structures of the diffusion process.
arXiv Detail & Related papers (2023-02-07T17:07:46Z) - Detecting Rotated Objects as Gaussian Distributions and Its 3-D
Generalization [81.29406957201458]
Existing detection methods commonly use a parameterized bounding box (BBox) to model and detect (horizontal) objects.
We argue that such a mechanism has fundamental limitations in building an effective regression loss for rotation detection.
We propose to model the rotated objects as Gaussian distributions.
We extend our approach from 2-D to 3-D with a tailored algorithm design to handle the heading estimation.
arXiv Detail & Related papers (2022-09-22T07:50:48Z) - Semiparametric Nonlinear Bipartite Graph Representation Learning with
Provable Guarantees [106.91654068632882]
We consider the bipartite graph and formalize its representation learning problem as a statistical estimation problem of parameters in a semiparametric exponential family distribution.
We show that the proposed objective is strongly convex in a neighborhood around the ground truth, so that a gradient descent-based method achieves linear convergence rate.
Our estimator is robust to any model misspecification within the exponential family, which is validated in extensive experiments.
arXiv Detail & Related papers (2020-03-02T16:40:36Z)
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.