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| Main Author: | |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2505.09071 |
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Table of Contents:
- Physics-based models often involve large systems of parametrized partial differential equations, where design parameters control various properties. However, high-fidelity simulations of such systems on large domains or with high grid resolution can be computationally expensive for the accurate evaluation of a large number of parameters. Reduced-order modeling has emerged as a solution to reduce the dimensionality of such problems. This work focuses on a nonlinear compression technique using a convolutional autoencoder for accelerating the solution of transport in porous media problems. The model demonstrates successful training, achieving a mean square error (MSE) on the order of \num{1e-3} for the validation data. For an unseen parameter set, the model exhibits mixed performance; it achieves acceptable accuracy for larger time steps but shows lower performance for earlier times. This issue could potentially be resolved by fine-tuning the network architecture.