Výsledky vyhledávání - "Andersen, Pal Østebø"

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History-Matching of Imbibition Flow in Multiscale Fractured Porous Media Using Physics-Informed Neural Networks (PINNs)

Autor: Abbasi, Jassem, Moseley, Ben, Kurotori, Takeshi, Jagtap, Ameya D., Kovscek, Anthony R., Hiorth, Aksel, Andersen, Pål Østebø

We propose a workflow based on physics-informed neural networks (PINNs) to model multiphase fluid flow in fractured porous media. After validating the workflow in forward and inverse modeling of a synthetic problem of flow in fractured porous media,

Externí odkaz: http://arxiv.org/abs/2410.20801

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Simulation and Prediction of Countercurrent Spontaneous Imbibition at Early and Late Times Using Physics-Informed Neural Networks

Autor: Abbasi, Jassem, Andersen, Pål Østebø

The application of Physics-Informed Neural Networks (PINNs) is investigated for the first time in solving the one-dimensional Countercurrent spontaneous imbibition (COUCSI) problem at both early and late time (i.e., before and after the imbibition fr

Externí odkaz: http://arxiv.org/abs/2306.05554

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Early- and late-time prediction of counter-current spontaneous imbibition, scaling analysis and estimation of the capillary diffusion coefficient

Autor: Andersen, Pål Østebø

Solutions are investigated for 1D linear counter-current spontaneous imbibition (COUSI). The diffusion problem is scaled to depend only on a normalized coefficient {\Lambda}_n (S_n ) with mean 1 and no other parameters. A dataset of 5500 functions {\

Externí odkaz: http://arxiv.org/abs/2211.07571

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Physical Activation Functions (PAFs): An Approach for More Efficient Induction of Physics into Physics-Informed Neural Networks (PINNs)

Autor: Abbasi, Jassem, Andersen, Pål Østebø

In recent years, the gap between Deep Learning (DL) methods and analytical or numerical approaches in scientific computing is tried to be filled by the evolution of Physics-Informed Neural Networks (PINNs). However, still, there are many complication

Externí odkaz: http://arxiv.org/abs/2205.14630

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