Search Results for author:
Found 8 papers, 5 papers with code
Using AI libraries for Incompressible Computational Fluid Dynamics
Recently, there has been a huge effort focused on developing highly efficient open source libraries to perform Artificial Intelligence (AI) related computations on different computer architectures (for example, CPUs, GPUs and new AI processors).
Solving the Discretised Multiphase Flow Equations with Interface Capturing on Structured Grids Using Machine Learning Libraries
The idea comes from the observation that convolutional layers can be used to express a discretisation as a neural network whose weights are determined by the numerical method, rather than by training, and hence, we refer to this approach as Neural Networks for PDEs (NN4PDEs).
An AI-based Domain-Decomposition Non-Intrusive Reduced-Order Model for Extended Domains applied to Multiphase Flow in Pipes
The whole framework is applied to multiphase slug flow in a horizontal pipe for which an AI-DDNIROM is trained on high-fidelity CFD simulations of a pipe of length 10 m with an aspect ratio of 13:1, and tested by simulating the flow for a pipe of length 98 m with an aspect ratio of almost 130:1.
Generative Network-Based Reduced-Order Model for Prediction, Data Assimilation and Uncertainty Quantification
The results show that the proposed GN-based ROM can efficiently quantify uncertainty and accurately match the measurements and the golden standard, using only a few unconditional simulations of the full-order numerical PDE model.
Data Assimilation Predictive GAN (DA-PredGAN): applied to determine the spread of COVID-19
To predict the spread of COVID-19 in an idealised town, we apply these methods to a compartmental model in epidemiology that is able to model space and time variations.
Digital twins based on bidirectional LSTM and GAN for modelling the COVID-19 pandemic
Here we introduce two digital twins of a SEIRS model applied to an idealised town.
Applying Convolutional Neural Networks to Data on Unstructured Meshes with Space-Filling Curves
The space-filling curves (SFCs) are used to find an ordering of the nodes or cells that can transform multi-dimensional solutions on unstructured meshes into a one-dimensional (1D) representation, to which 1D convolutional layers can then be applied.
An autoencoder-based reduced-order model for eigenvalue problems with application to neutron diffusion
Using an autoencoder for dimensionality reduction, this paper presents a novel projection-based reduced-order model for eigenvalue problems.
