A reciprocity-based cross-directional compensation framework estimates a shared propagation baseline from opposing ultrasonic backscatter profiles to reduce distance-dependent bias while preserving local scattering variations in heterogeneous media.
Ben Britton, Tea-Sung Jun, Weimin Gan, Michael Hofmann, Fionn P.E
8 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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A new differentiable reconstruction method uses symmetrized hyperspherical harmonics on quaternions plus two- and three-point descriptors to generate 3D microstructures from 2D data, demonstrated on aluminum alloy with L-BFGS-B optimization.
An iterative optimization of interstitial chemical potential under partial equilibrium assumption produces thermodynamically consistent interstitial concentration maps from substitutional microscopy data and bulk measurements.
An extended dual-solute framework predicts co-segregation bounds in multicomponent alloys by machine-learning pairwise segregation energies that include solute-solute interactions and is validated on magnesium systems.
MSI is a multimodal representation learning framework that identifies key microstructural features governing mechanical behavior in structural alloys from spatial observations.
ML model using ideal entropy plus simulation features (energy above hull, heat capacity change, icosahedral fraction) predicts metallic glass critical cooling rates with R²=0.78 in leave-one-chemical-system-out cross-validation on 34 alloys.
Forward-operator geometry analysis shows identifiability limits in ultrasonic models arise from parameter coupling, anisotropic sensitivity, and stochastic variability, with combined observables improving conditioning.
TD-MARL uses shared topological states and invariants to coordinate soft robots and reduce entanglement risk, outperforming standard DRL in simulated convergence and anti-winding performance.
citing papers explorer
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A Reciprocity-Based Signal Compensation Framework for Ultrasonic Backscatter Measurements in Heterogeneous Scattering Media
A reciprocity-based cross-directional compensation framework estimates a shared propagation baseline from opposing ultrasonic backscatter profiles to reduce distance-dependent bias while preserving local scattering variations in heterogeneous media.
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Generative reconstruction of 2D and 3D polycrystalline microstructures using symmetrized hyperspherical harmonics
A new differentiable reconstruction method uses symmetrized hyperspherical harmonics on quaternions plus two- and three-point descriptors to generate 3D microstructures from 2D data, demonstrated on aluminum alloy with L-BFGS-B optimization.
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Iterative Thermodynamic Augmentation of Spatially Resolved Analytic Microscopy for Fast-Diffusing Solutes
An iterative optimization of interstitial chemical potential under partial equilibrium assumption produces thermodynamically consistent interstitial concentration maps from substitutional microscopy data and bulk measurements.
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Predicting co-segregation in multicomponent alloys with solute-solute interactions
An extended dual-solute framework predicts co-segregation bounds in multicomponent alloys by machine-learning pairwise segregation energies that include solute-solute interactions and is validated on magnesium systems.
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Interpretable Material Spatial Intelligence for Discovery of Governing Microstructural Features
MSI is a multimodal representation learning framework that identifies key microstructural features governing mechanical behavior in structural alloys from spatial observations.
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Machine learning metallic glass critical cooling rates through elemental and molecular simulation based featurization
ML model using ideal entropy plus simulation features (energy above hull, heat capacity change, icosahedral fraction) predicts metallic glass critical cooling rates with R²=0.78 in leave-one-chemical-system-out cross-validation on 34 alloys.
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Identifiability Limits in Ultrasonic Microstructure Characterisation: A Canonical and Stochastic Framework
Forward-operator geometry analysis shows identifiability limits in ultrasonic models arise from parameter coupling, anisotropic sensitivity, and stochastic variability, with combined observables improving conditioning.
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Topology-Driven Anti-Entanglement Control for Soft Robots
TD-MARL uses shared topological states and invariants to coordinate soft robots and reduce entanglement risk, outperforming standard DRL in simulated convergence and anti-winding performance.