1 citations · 1 across the 3 of their papers we have counts for
7 papers
Neural-Network Closures for Complex-Shaped Particles in the Force-Coupling Method
Marco Laudato
A data-driven surrogate framework to accelerate particle-resolved modelling of quasi-dilute suspensions of rigid, non-spherical particles in Stokes flow is introduced. A regularize…
A Neural-Operator Surrogate for Platelet Deformation Across Capillary Numbers
Marco Laudato
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 train a DeepONe…
Neural Operator Modeling of Platelet Geometry and Stress in Shear Flow
Marco Laudato, Luca Manzari, Khemraj Shukla
Thrombosis involves processes spanning large-scale fluid flow to sub-cellular events such as platelet activation. Traditional CFD approaches often treat blood as a continuum, which…
Sparse wavefield reconstruction and denoising with boostlets
Elias Zea, Marco Laudato, Joakim Andén
Boostlets are spatiotemporal functions that decompose nondispersive wavefields into a collection of localized waveforms parametrized by dilations, hyperbolic rotations, and transla…
High-Fidelity Description of Platelet Deformation Using a Neural Operator
Marco Laudato, Luca Manzari, Khemraj Shukla
The goal of this work is to investigate the capability of a neural operator (DeepONet) to accurately capture the complex deformation of a platelet's membrane under shear flow. The…
Covariant Momentum Map Thermodynamics for Parametrized Field Theories
Goffredo Chirco, Marco Laudato, Fabio M. Mele
A general-covariant statistical framework capable of describing classical fluctuations of the gravitational field is a thorny open problem in theoretical physics, yet ultimately ne…