Using a Data Driven Approach to Predict Waves Generated by Gravity Driven Mass Flows

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Wave breaking is a complex physical process about which open questions remain. For some applications, it is critical to include breaking effects in phase-resolved envelope-based wave models such as the non-linear Schr & ouml;dinger. A promising approach is ...

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In a recent discovery (Wen et al 2022 Phys. Rev. Lett. 129 045001), streaming waves were found in multipactor-induced plasma discharges. However, due to the limitations of a 1D simulation setup, these waves displayed only transverse dynamics. In this lette ...

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We introduce a reconfigurable medium for the manipulation of elastic propagation properties of Lamb waves. It is based on a shape memory polymer (SMP) with temperature-dependent Young's modulus. Waves are excited by a laser pulse and detected by a laser vi ...

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We experimentally demonstrate the coherent propagation of magnon polarons, the hybridized excitations formed by the coupling of spin waves and Rayleigh/Love surface acoustic waves, in a heterostructure consisting of ferroelectric BaTiO3 and ferromagnetic L ...

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Acoustic emission (AE) monitoring is a useful technique to monitor the health of a structure continuously, helping to prevent potential failure. AE are elastic waves produced and emitted during fracture processes inside a material and are recorded by senso ...

Using a Data Driven Approach to Predict Waves Generated by Gravity Driven Mass Flows

Chattez avec Graph Search

A machine learning architecture for including wave breaking in envelope-type wave models

Optothermal shaping of lamb waves with square and spiral phase fronts

Task-driven neural network models predict neural dynamics of proprioception: Experimental data, activations and predictions of neural network models

Electric-field-induced second-order nonlinear processes in stoichiometric silicon nitride

Reduced Training Data for Laser Ultrasound Signal Interpretation by Neural Networks

Statistical Inference for Inverse Problems: From Sparsity-Based Methods to Neural Networks

Infusing structured knowledge priors in neural models for sample-efficient symbolic reasoning

Oblique streaming waves observed in multipactor-induced plasma discharge above a dielectric surface

Stochastic particle transport by deep-water irregular breaking waves

Optimization of elastic wave propagation in a reconfigurable medium by genetic algorithms with adaptive mutation probability

Nonlinear interactions of ion acoustic waves explored using fast imaging decompositions

Non-contact robotic manipulation of floating objects: exploiting emergent limit cycles

Temporal Prediction of Landslide-Generated Waves Using a Theoretical-Statistical Combined Method

Natural slicks on Lake Geneva: From small-scale effects to large-scale formation and distribution

Ionospheric Variations Induced by Thunderstorms in the Central Region of Argentina during the RELAMPAGO–CACTI Campaign

Long-distance coherent propagation of magnon polarons in a ferroelectric-ferromagnetic heterostructure

Automatic and high-precision microseismic monitoring of progressive failure prior, during, and after tunnel excavation

Fundamental Limits in Statistical Learning Problems: Block Models and Neural Networks

Stable parameterization of continuous and piecewise-linear functions

Automatic and high-precision acoustic emission-based structural health monitoring of concrete structures