Reliable multiscale models of thrombosis require platelet-scale fidelity at organ-scale cost, a gap that scientific machine learning has the potential to narrow. We trained a DeepONet surrogate on platelet dynamics generated with LAMMPS for platelets spanning ten elastic moduli and capillary numbers (0.07–0.77). The network takes as input the wall shear stress, bond stiffness, time, and initial particle coordinates and returns the full three-dimensional deformation of the membrane. Mean-squared-error minimization with Adam and adaptive learning-rate decay yields a median displacement error below 1%, a 90th percentile below 3%, and a worst case below 4% over the entire calibrated range while accelerating computation by four to five orders of magnitude. Leave-extremes-out retraining shows acceptable extrapolation: the held-out stiffest and most compliant platelets retain sub-3% median error and an 8% maximum. Error peaks coincide with transient membrane self-contact, suggesting improvements via graph neural trunks and physics-informed torque regularization. These results represent a first demonstration of how the surrogate has the potential for coupling with continuum CFD, enabling future platelet-resolved hemodynamic simulations in patient-specific geometries and opening new avenues for predictive thrombosis modeling.

This paper introduces the boostlet transform to analyze and reconstruct spatiotemporal acoustic fields measured in 2D space-time. The transform builds upon the insight that sparse multi-scale representations learned from natural wavefields perform geometric transformations that preserve the dispersion relation. The boostlet transform decomposes a spatiotemporal wavefield using a collection of wavelet-like functions parametrized by dilations, hyperbolic rotations, and translations in space-time. From a physical viewpoint, boostlets encompass global and localized waveforms with variable band-limited frequency and phase-speed content. We show transform applications of wavefront segmentation and sparse reconstruction of room impulse responses. In particular, we find that boostlet decompositions excel at representing localized wavefront phenomena typical of the early part of such room recordings. At the same time, plane waves perform equally as well as or better than boostlets in the late part.

Boostlets are spatiotemporal functions that decompose nondispersive wavefields into a collection of localized waveforms parametrized by dilations, hyperbolic rotations, and translations. We study the sparsity properties of boostlets and find that the resulting decompositions are significantly sparser than those of other state-of-the-art representation systems, such as wavelets and shearlets. This translates into improved denoising performance when hard-thresholding the boostlet coefficients. The results suggest that boostlets offer a natural framework for sparsely decomposing wavefields in unified space–time.

Some plain considerations are provided on the influence of axial deformation on the stability of the upper equilibrium position of the Kapitza pendulum with respect to the linearisation or non-linearisation of the associated Lagrange's equations. Following a very uncomplicated approach and fully accounting for the non-linearity of the problem, it is shown that in the case of the extensible Kapitza pendulum the dynamical behaviour of the system cannot be always correctly captured by a simple linearisation about the upper equilibrium point and a phenomenon related to the degree of approximation can take place for this dynamic system that replicates what happens in the case of the stability of equilibrium of simple axially extensible systems. Also, it is remarked that the introduction of axial deformation may play the same role as the addition of damping.

Obstructive sleep apnea syndrome (OSAS) is referring to partial or complete cessation of airflow during sleep due to upper pharyngeal airway collapse. Snoring is often associated to such sleep-induced apnea as the result of the strong coupling and interaction between the respiratory airflow and the flexible tissue of the upper airway, resulting in self-excited oscillations of the soft tissue. It can occur at the level of the soft palate but also at the lower level of the pharyngeal airway. The knowledge of the tissue behavior subject to a particular airway flow is relevant for understanding the underlying physical mechanism of OSA. However, in-vivo measurements are usually not practical. A 3D fluid-structure interaction model for the human uvula-palatal system relevant to OSAS based on simplified geometries is utilized in the present study. Numerical simulations are performed to assess the influence of gravity and the rigidity of the soft tissue on the oscillatory dynamics. Meanwhile, the vortex dynamics and the structural modal frequency response are investigated for the coupled fluid-structure system as well.

Collapsible tubes can be employed to study the sound generation mechanism in the human respiratory system. The goals of this work are (a) to determine the airflow characteristics connected to three different collapse states of a physiological tube and (b) to find a relation between the sound power radiated by the tube and its collapse state. The methodology is based on the implementation of computational fluid dynamics simulation on experimentally validated geometries. The flow is characterized by a radical change of behavior before and after the contact of the lumen. The maximum of the sound power radiated corresponds to the post-buckling configuration. The idea of an acoustic tube law is proposed. The presented results are relevant to the study of self-excited oscillations and wheezing sounds in the lungs.

The onset of self-excited oscillations in airways and blood vessels is a common phenomenon in the human body, connected to both normal and pathological conditions. A recent experimental investigation has shown that the onset of self-excited oscillations happens for values of the intramural pressure close to the contact critical pressure. The goal of this work is to analyse the dependence of the contact critical pressure on the vessel’s geometric parameters. The methodology is based on the implementation of an experimentally validated computational model of a collapsible tube. The results confirm the correlation between the contact critical pressure and the onset of self-excited oscillations in collapsible tubes. Moreover, a set of general equations to compute the contact critical pressure and the corresponding areas of collapsible tubes with arbitrary geometries has been derived.

The behaviour of collapsed or stenotic vessels in the human body can be studied by means of simplified geometries like a collapsible tube. The objective of this work is to determine the value of the buckling critical pressure of a collapsible tube by employing Landau’s theory of phase transition. The methodology is based on the implementation of an experimentally validated 3D numerical model of a collapsible tube. The buckling critical pressure is estimated for different values of geometric parameters of the system by treating the relation between the intramural pressure and the area of the central cross-section as the order parameter function of the system. The results show the dependence of the buckling critical pressures on the geometric parameters of a collapsible tube. General non-dimensional equations for the buckling critical pressures are derived. The advantage of this method is that it does not require any geometric assumption, but it is solely based on the observation that the buckling of a collapsible tube can be treated as a second-order phase transition. The investigated geometric and elastic parameters are sensible for biomedical application, with particular interest to the study of the bronchial tree under pathophysiological conditions like asthma.

Flexible tubes are simple yet powerful tools for the modeling of respiratory and circulatory systems [1, 2]. In the last decade, access to high-performance computational resources has allowed the implementation of realistic numerical models of human vessels based on Computational Fluid Dynamics (CFD) and Fluid-Structure Interaction (FSI) simulations. Interestingly, the recently proven correlation [3] between CFD observables (like the pharyngeal airway's resistance) and clinical data (such as the Apnoea Hypopnea Index in Obstructive Sleep Apnoea patients) suggests a possible development of diagnostic methods based on numerical simulations. This work aims to investigate the extension of these correlations to the aeroacoustics characteristics of flexible conduits by means of a fully coupled FSI numerical model based on the Large Eddy Simulation method. These results have relevant applications to studying diseases of the human upper vocal tract, voice production, obstructive sleep apnoea, and adventitious lung sounds, such as wheezing and crackling.

[1] Grotberg, J. B., & Jensen, O. E. (2004). Annu. Rev. Fluid Mech., 36, 121–147.

[2] Schwartz, A. R., & Smith, P. L. (2013). The Journal of Physiology, 591(Pt 9), 2229.

[3] Schickhofer L., Malinen J., Mihaescu M., J. Acoust. Soc. Am. - JASA, 145 (4): 2049–2061, 2019. https://doi.org/10.1121/1.5095250

Subjects with bicuspid aortic valves (BAV) are at risk of developing valve dysfunction and need regular clinical imaging surveillance. Management of BAV involves manual and time-consuming segmentation of the aorta for assessing left ventricular function, jet velocity, gradient, shear stress, and valve area with aortic valve stenosis. This paper aims to employ machine learning-based (ML) segmentation as a potential for improved BAV assessment and reducing manual bias. The focus is on quantifying the relationship between valve morphology and vortical structures, and analyzing how valve morphology influences the aorta’s susceptibility to shear stress that may lead to valve incompetence. The ML-based segmentation that is employed is trained on whole-body Computed Tomography (CT). Magnetic Resonance Imaging (MRI) is acquired from six subjects, three with tricuspid aortic valves (TAV) and three functionally BAV, with right–left leaflet fusion. These are used for segmentation of the cardiovascular system and delineation of four-dimensional phase-contrast magnetic resonance imaging (4D-PCMRI) for quantification of vortical structures and wall shear stress. The ML-based segmentation model exhibits a high Dice score (0.86) for the heart organ, indicating a robust segmentation. However, the Dice score for the thoracic aorta is comparatively poor (0.72). It is found that wall shear stress is predominantly symmetric in TAVs. BAVs exhibit highly asymmetric wall shear stress, with the region opposite the fused coronary leaflets experiencing elevated tangential wall shear stress. This is due to the higher tangential velocity explained by helical flow, proximally of the sinutubal junction of the ascending aorta. ML-based segmentation not only reduces the runtime of assessing the hemodynamic effectiveness, but also identifies the significance of the tangential wall shear stress in addition to the axial wall shear stress that may lead to the progression of valve incompetence in BAVs, which could guide potential adjustments in surgical interventions.