Identifiability limits in ultrasonic microstructure characterization are governed by forward-map structure and intrinsic stochastic variability, with combined observables improving conditioning through complementary sensitivities.
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Thermal-crystal plasticity simulations combined with dynamic mode decomposition show that thermal stress responses under cyclic loading can be compactly represented as superpositions of frequency-dependent temporal modes.
A reduced-integration stabilization for VEM at finite strains is introduced using scaled boundary parametrization and one-point analytical integration per section, validated on patch tests and nonlinear examples.
Uncertainty-aware neural networks using Gaussian negative log-likelihood and dropout are applied to predict intrinsic magnetic properties and coercivity via graph neural networks in permanent magnet research.
citing papers explorer
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Identifiability Limits in Ultrasonic Microstructure Characterisation: A Canonical and Stochastic Framework
Identifiability limits in ultrasonic microstructure characterization are governed by forward-map structure and intrinsic stochastic variability, with combined observables improving conditioning through complementary sensitivities.
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Frequency-dependent stress response under thermal cycle: A thermal-crystal plasticity and dynamic mode decomposition study
Thermal-crystal plasticity simulations combined with dynamic mode decomposition show that thermal stress responses under cyclic loading can be compactly represented as superpositions of frequency-dependent temporal modes.
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Reduced integration with scaled boundary parametrization for virtual elements at finite strains
A reduced-integration stabilization for VEM at finite strains is introduced using scaled boundary parametrization and one-point analytical integration per section, validated on patch tests and nonlinear examples.
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Modelling magnetic material properties with uncertainty-aware neural networks
Uncertainty-aware neural networks using Gaussian negative log-likelihood and dropout are applied to predict intrinsic magnetic properties and coercivity via graph neural networks in permanent magnet research.