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Simulation-based pre-training improves small-sample LIBS quantification through correct line–element association

https://doi.org/10.2139/ssrn.7597604
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inferredcites →https://doi.org/10.1007/s10462-023-10590-5openalex-snapshot
Application of deep learning in laser-induced breakdown spectroscopy: a review
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Development of a deep-sea laser-induced breakdown spectrometer for in situ multi-element chemical analysis
inferredcites →https://doi.org/10.1016/j.dsr.2020.103232openalex-snapshot
Quantitative in situ mapping of elements in deep-sea hydrothermal vents using laser-induced breakdown spectroscopy and multivariate analysis
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Adaptive Batch Normalization for practical domain adaptation
inferredcites →https://doi.org/10.1016/j.sab.2009.11.005openalex-snapshot
Local Thermodynamic Equilibrium in Laser-Induced Breakdown Spectroscopy: Beyond the McWhirter criterion
inferredcites →https://doi.org/10.1016/j.sab.2009.11.006openalex-snapshot
Calibration-Free Laser-Induced Breakdown Spectroscopy: State of the art
inferredcites →https://doi.org/10.1016/j.sab.2022.106490openalex-snapshot
Incorporating domain knowledge into machine learning for laser-induced breakdown spectroscopy quantification
inferredcites →https://doi.org/10.1016/j.sab.2023.106839openalex-snapshot
Sharing calibration information among laser-induced breakdown spectroscopy instruments using spectral line binning and calibration transfer
inferredcites →https://doi.org/10.1016/j.sab.2024.107082openalex-snapshot
Towards real-time calibration-free LIBS supported by machine learning
inferredcites →https://doi.org/10.1016/j.sab.2025.107198openalex-snapshot
Online analysis of coal particle flow by laser-induced breakdown spectroscopy based on pelletized coal calibration samples and feature-based transfer learning
inferredcites →https://doi.org/10.1016/j.talanta.2023.124946openalex-snapshot
Interpreting convolutional neural network classifiers applied to laser-induced breakdown optical emission spectra
inferredcites →https://doi.org/10.1016/j.talanta.2024.127192openalex-snapshot
Enhancing prediction stability and performance in LIBS analysis using custom CNN architectures
inferredcites →https://doi.org/10.1039/d2ja00180bopenalex-snapshot
Improving laser-induced breakdown spectroscopy regression models via transfer learning
inferredcites →https://doi.org/10.1039/d2ja00182aopenalex-snapshot
A transferred multitask regularization convolutional neural network (TrMR-CNN) for laser-induced breakdown spectroscopy quantitative analysis
inferredcites →https://doi.org/10.1039/d2ja00406bopenalex-snapshot
Spectral library transfer between distinct laser-induced breakdown spectroscopy systems trained on simultaneous measurements
inferredcites →https://doi.org/10.1039/d4ja00407hopenalex-snapshot
Laser-induced breakdown spectroscopy chemometrics for ChemCam Mars in situ data analysis based on deep learning and pretrained-model-based transfer learning
inferredcites →https://doi.org/10.1039/d4ja00412dopenalex-snapshot
Data augmentation using GANN in the quantitative LIBS analysis of scarce samples: a case study on polymetallic nodules from 5000 m ocean depth
inferredcites →https://doi.org/10.1039/d4ja00459kopenalex-snapshot
TrCSL: a transferred CNN-SE-LSTM model for high-accuracy quantitative analysis of laser-induced breakdown spectroscopy with small samples
inferredcites →https://doi.org/10.1039/d5ja00401bopenalex-snapshot
Many-to-many transfer of LIBS spectra across multiple experimental conditions
inferredcites →https://doi.org/10.1039/d5ja00490jopenalex-snapshot
Improving small-sample performance in portable LIBS analysis with transfer learning and synthetic data-driven CNN models
inferredcites →https://doi.org/10.1051/epjconf/202328801005openalex-snapshot
Trustworthiness of Laser-Induced Breakdown Spectroscopy Predictions via Simulation-based Synthetic Data Augmentation and Multitask Learning
inferredcites →https://doi.org/10.1063/5.0205572openalex-snapshot
ANN based LIBS models for quasi-experimental spectra relevant for materials for next-step fusion reactors
inferredcites →https://doi.org/10.1109/iros.2017.8202133openalex-snapshot
Domain randomization for transferring deep neural networks from simulation to the real world
inferredcites →https://doi.org/10.1109/tkde.2009.191openalex-snapshot
A Survey on Transfer Learning
inferredcites →https://doi.org/10.1111/ecog.02881openalex-snapshot
Cross‐validation strategies for data with temporal, spatial, hierarchical, or phylogenetic structure
inferredcites →https://doi.org/10.1117/12.2520589openalex-snapshot
Super-convergence: very fast training of neural networks using large learning rates
inferredcites →https://doi.org/10.1214/aoms/1177703732openalex-snapshot
Robust Estimation of a Location Parameter
inferredcites →https://doi.org/10.1214/aos/1176344552openalex-snapshot
Bootstrap Methods: Another Look at the Jackknife
inferredcites →https://doi.org/10.1366/0003702991947612openalex-snapshot
New Procedure for Quantitative Elemental Analysis by Laser-Induced Plasma Spectroscopy
inferredcites →https://doi.org/10.1366/11-06574openalex-snapshot
Laser-Induced Breakdown Spectroscopy (LIBS), Part II: Review of Instrumental and Methodological Approaches to Material Analysis and Applications to Different Fields
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