Cover Image

Physics-guided impact localisation and force estimation in composite plates with uncertainty quantification

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Xiao, Dong, Sharif-Khodaei, Zahra, Aliabadi, M. H.
Format:	Preprint
Published:	2025
Subjects:	Data Analysis, Statistics and Probability Materials Science Machine Learning Applied Physics
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Comparative study of ensemble-based uncertainty quantification methods for neural network interatomic potentials
by: Kurniawan, Yonatan, et al.
Published: (2025)

Improved precision and accuracy of electron energy-loss spectroscopy quantification via fine structure fitting with constrained optimization
by: Jannis, Daen, et al.
Published: (2024)

Application of machine learning in grain-related clustering of Laue spots in a polycrystalline energy dispersive Laue pattern
by: Tosson, Amir, et al.
Published: (2024)

Beyond empirical models: Discovering new constitutive laws in solids with graph-based equation discovery
by: Xu, Hao, et al.
Published: (2025)

Autonomous convergence of STM control parameters using Bayesian Optimization
by: Narasimha, Ganesh, et al.
Published: (2023)

Estimation of material parameter uncertainties using probabilistic and interval approaches
by: Most, Thomas
Published: (2024)

Physics-guided machine learning predicts the planet-scale performance of solar farms with sparse, heterogeneous, public data
by: Jahangir, Jabir Bin, et al.
Published: (2024)

An information-matching approach to optimal experimental design and active learning
by: Kurniawan, Yonatan, et al.
Published: (2024)

Physics-constrained polynomial chaos expansion for scientific machine learning and uncertainty quantification
by: Sharma, Himanshu, et al.
Published: (2024)

Peak separation methods for inverse photoelectron spectra: Comparing second derivative, curve fitting, and deconvolution analyses
by: Nakazawa, Ryotaro, et al.
Published: (2025)

Investigating the missing wedge problem in small-angle x-ray scattering tensor tomography across real and reciprocal space
by: Nielsen, Leonard C., et al.
Published: (2024)

Recommendations and tools to enable reproducibility in 2D materials research
by: Bøggild, Peter, et al.
Published: (2024)

Physics-based linear regression for high-dimensional forward uncertainty quantification
by: Wang, Ziqi
Published: (2024)

KAN-Matrix: Visualizing Nonlinear Pairwise and Multivariate Contributions for Physical Insight
by: De la Fuente, Luis A., et al.
Published: (2025)

A Small Step for Epitaxy, a Large Step Towards Twist Angle Control in 2D Heterostructures
by: Maßmeyer, Oliver, et al.
Published: (2024)

LOMS.cz: A computational platform for high-throughput Classical and Combinatorial Judd-Ofelt analysis and rare-earth spectroscopy
by: Hrabovský, Jan, et al.
Published: (2025)

Enhancing Reconstruction Capability of Wavelet Transform Amorphous Radial Distribution Function via Machine Learning Assisted Parameter Tuning
by: Senjaya, Deriyan, et al.
Published: (2025)

Machine Learning Models for Accurately Predicting Properties of CsPbCl3 Perovskite Quantum Dots
by: Çadırcı, Mehmet Sıddık, et al.
Published: (2024)

Boosting SISSO Performance on Small Sample Datasets by Using Random Forests Prescreening for Complex Feature Selection
by: Jiang, Xiaolin, et al.
Published: (2024)

Securing generative artificial intelligence with parallel magnetic tunnel junction true randomness
by: Bao, Youwei, et al.
Published: (2025)

Autonomous Probe Microscopy with Robust Bag-of-Features Multi-Objective Bayesian Optimization: Pareto-Front Mapping of Nanoscale Structure-Property Trade-Offs
by: Barakati, Kamyar, et al.
Published: (2026)

Training-set-free two-stage deep learning for spectroscopic data de-noising
by: Huang, Dongchen, et al.
Published: (2024)

Quantitative approaches for multi-scale structural analysis with atomic resolution electron microscopy
by: Schnitzer, Noah, et al.
Published: (2025)

Accurate Machine Learning Predictions of Coercivity in High-Performance Permanent Magnets
by: Bhandari, Churna, et al.
Published: (2023)

Closer look at sum uncertainty relations and related relations
by: Urbanowski, Krzysztof
Published: (2025)

A multiscale Bayesian approach to quantification and denoising of energy-dispersive x-ray data
by: Torruella, Pau, et al.
Published: (2025)

Hitchhiker's guide on the relation of Energy-Based Models with other generative models, sampling and statistical physics: a comprehensive review
by: Carbone, Davide
Published: (2024)

A Physics-Augmented Machine Learning Constitutive Model for Damage in Solids
by: Amiri-Hezaveh, Amirhossein, et al.
Published: (2025)

Physics-Informed Tree Search for High-Dimensional Computational Design
by: Banik, Suvo, et al.
Published: (2026)

Bayesian approach for uncertainty quantification of hybrid spectral unmixing in $γ$-ray spectrometry
by: Phan, Dinh Triem, et al.
Published: (2026)

Generating gradients in the energy landscape using rectified linear type cost functions for efficiently solving 0/1 matrix factorization in Simulated Annealing
by: Konoshima, Makiko, et al.
Published: (2023)

Process signature-driven high spatio-temporal resolution alignment of multimodal data
by: Hanchate, Abhishek, et al.
Published: (2024)

The Moving Beam Diffraction Geometry: the DIAD Application of a Diffraction Scanning-Probe
by: Leonardi, Alberto, et al.
Published: (2025)

Roadmap on Data-Centric Materials Science
by: Bauer, Stefan, et al.
Published: (2024)

A model for positron annihilation in multi-layer systems by solving the diffusion equation using different positron affinities
by: Mathes, Lucian, et al.
Published: (2025)

Ensemble-Based Data Assimilation for Material Model Characterization in High-Velocity Impact
by: Jin, Rong, et al.
Published: (2025)

Composition Design of Shape Memory Ceramics based on Gaussian Processes
by: Pandey, Ashutosh, et al.
Published: (2025)

Link Statistics of Dislocation Network during Strain Hardening
by: Akhondzadeh, Sh., et al.
Published: (2025)

3D deep learning for enhanced atom probe tomography analysis of nanoscale microstructures
by: Yu, Jiwei, et al.
Published: (2024)

Clustering in atom probe tomography data: coordination number metric, percolation-based parameter scaling, and size effects
by: Lazarev, Mykola, et al.
Published: (2026)