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Found 10 papers, 5 papers with code
Integrating supervised and unsupervised learning approaches to unveil critical process inputs
This study introduces a machine learning framework tailored to large-scale industrial processes characterized by a plethora of numerical and categorical inputs.
Multi-compartment poroelastic models of perfused biological soft tissues: implementation in FEniCSx
Several benchmark cases are studied.
Convolution, aggregation and attention based deep neural networks for accelerating simulations in mechanics
We study and compare the performance of all three networks on two benchmark examples, and show their capabilities to accurately predict the non-linear mechanical responses of soft bodies.
Oncology and mechanics: landmark studies and promising clinical applications
Mechanical phenomena in cancer have been studied in-depth over the last decades, and their clinical applications are starting to be understood.
MAgNET: A Graph U-Net Architecture for Mesh-Based Simulations
In this work, we present a novel encoder-decoder geometric deep learning framework called MAgNET, which extends the well-known convolutional neural networks to accommodate arbitrary graph-structured data.
Probabilistic Deep Learning for Real-Time Large Deformation Simulations
For many novel applications, such as patient-specific computer-aided surgery, conventional solution techniques of the underlying nonlinear problems are usually computationally too expensive and are lacking information about how certain can we be about their predictions.
Machine learning in the social and health sciences
The uptake of machine learning (ML) approaches in the social and health sciences has been rather slow, and research using ML for social and health research questions remains fragmented.
Hierarchical a posteriori error estimation of Bank-Weiser type in the FEniCS Project
The focus of this contribution is to describe a novel implementation of hierarchical estimators of the Bank-Weiser type in a modern high-level finite element software with automatic code generation capabilities.
Code Generation
Numerical Analysis
Computational Engineering, Finance, and Science
Numerical Analysis
A Bayesian multiscale CNN framework to predict local stress fields in structures with microscale features
Multiscale computational modelling is challenging due to the high computational cost of direct numerical simulation by finite elements.
Quantifying discretization errors for soft-tissue simulation in computer assisted surgery: a preliminary study
The impact of mesh quality and density on the accuracy of the FE solution can be quantified with \emph{a posteriori} error estimates.
Computational Engineering, Finance, and Science
