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We propose a unified data-driven reduced order model (ROM) that bridges the performance gap between linear and nonlinear manifold approaches.
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Kim, Y., Choi, Y., Widemann, D. & Zohdi, T · 2021
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Kim, Y., Wang, K. & Choi, Y · 2021
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Microfluidic investigation of salinity-induced oil recovery in porous media during chemical flooding
Park, S. W., Lee, J., Yoon, H. & Shin, S · 2021
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A locally conservative mixed finite element framework for coupled hydro-mechanical-chemical processes in heterogeneous porous media
Kadeethum, T., Lee, S., Ballarin, F., Choo, J. & Nick, H · 2021
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Choi, Y., Brown, P., Arrighi, W., Anderson, R. & Huynh, K · 2021
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Kadeethum, T., Ballarin, F. & Bouklas, N · 2021
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Barlow twins: Self-supervised learning via redundancy reduction
Zbontar, J., Jing, L., Misra, I., LeCun, Y. & Deny, S · 2021
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Non-intrusive reduced-order modeling using uncertainty-aware deep neural networks and proper orthogonal decomposition: Application to flood modeling
Jacquier, P., Abdedou, A., Delmas, V. & Soulaïmani, A · 2021
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A framework for data-driven solution and parameter estimation of pdes using conditional generative adversarial networks
Kadeethum, T. et al · 2021
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Non-intrusive reduced order modeling of natural convection in porous media using convolutional autoencoders: comparison with linear subspace techniques
Kadeethum, T. et al · 2022
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Reduced order models for lagrangian hydrodynamics
Copeland, D., Cheung, S., Huynh, K. & Choi, Y · 2022
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