Hugo Hervé-Côte, Frédéric Dupont-Marillia, Pierre Bélanger
Automatic flaw detection in sectoral scans using machine learning
Ultrasonics
paper pioneers novel approach electromagnetic em system analysis synergistically combining bayesian neural networks bnns informed latin hypercube sampling lhs advanced thermalmechanical surrogate modeling within comsol simulations highfrequency lowpass filter modeling methodology transcends traditional em characterization integrating physical dimension variability thermal effects mechanical deformation realworld operational conditions thereby achieving significant leap predictive modeling fidelity rigorous evaluation using mean squared error mse maximum learning error mle maximum test error mte metrics well comprehensive validation unseen data models robustness generalization capability demonstrated research challenges conventional methods offering nuanced understanding multiphysical phenomena enhance reliability resilience electronic component design optimization integration thermal variables alongside dimensional parameters marks novel paradigm filter performance analysis significantly improving simulation accuracy findings contribute body knowledge em diagnostics complexenvironment analysis also pave way future investigations fusion machine learning computational physics promising transformative impacts across various applications telecommunications medical devices