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Found 8 papers, 0 papers with code
Elastic shape analysis computations for clustering left atrial appendage geometries of atrial fibrillation patients
Morphological variations in the left atrial appendage (LAA) are associated with different levels of ischemic stroke risk for patients with atrial fibrillation (AF).
DIMON: Learning Solution Operators of Partial Differential Equations on a Diffeomorphic Family of Domains
DIMON is based on transporting a given problem (initial/boundary conditions and domain $\Omega_{\theta}$) to a problem on a reference domain $\Omega_{0}$, where training data from multiple problems is used to learn the map to the solution on $\Omega_{0}$, which is then re-mapped to the original domain $\Omega_{\theta}$.
A Generative Modeling Framework for Inferring Families of Biomechanical Constitutive Laws in Data-Sparse Regimes
Quantifying biomechanical properties of the human vasculature could deepen our understanding of cardiovascular diseases.
Interfacing Finite Elements with Deep Neural Operators for Fast Multiscale Modeling of Mechanics Problems
In this work, we explore the idea of multiscale modeling with machine learning and employ DeepONet, a neural operator, as an efficient surrogate of the expensive solver.
Simulating progressive intramural damage leading to aortic dissection using an operator-regression neural network
Aortic dissection progresses via delamination of the medial layer of the wall.
A physics-informed variational DeepONet for predicting the crack path in brittle materials
We propose a physics-informed variational formulation of DeepONet (V-DeepONet) for brittle fracture analysis.
Physics-informed neural networks (PINNs) for fluid mechanics: A review
Despite the significant progress over the last 50 years in simulating flow problems using numerical discretization of the Navier-Stokes equations (NSE), we still cannot incorporate seamlessly noisy data into existing algorithms, mesh-generation is complex, and we cannot tackle high-dimensional problems governed by parametrized NSE.
Physics-Informed Neural Networks for Nonhomogeneous Material Identification in Elasticity Imaging
We apply Physics-Informed Neural Networks (PINNs) for solving identification problems of nonhomogeneous materials.
