Search Results for author:
Found 6 papers, 1 papers with code
Efficient machine-learning surrogates for large-scale geological carbon and energy storage
Geological carbon and energy storage are pivotal for achieving net-zero carbon emissions and addressing climate change.
Progressive reduced order modeling: empowering data-driven modeling with selective knowledge transfer
Data-driven modeling can suffer from a constant demand for data, leading to reduced accuracy and impractical for engineering applications due to the high cost and scarcity of information.
Reduced order modeling for flow and transport problems with Barlow Twins self-supervised learning
Through a series of benchmark problems of natural convection in porous media, BT-AE performs better than the previous DL-ROM framework by providing comparable results to POD-based approaches for problems where the solution lies within a linear subspace as well as DL-ROM autoencoder-based techniques where the solution lies on a nonlinear manifold; consequently, bridges the gap between linear and nonlinear reduced manifolds.
A framework for data-driven solution and parameter estimation of PDEs using conditional generative adversarial networks
This work is the first to employ and adapt the image-to-image translation concept based on conditional generative adversarial networks (cGAN) towards learning a forward and an inverse solution operator of partial differential equations (PDEs).
Physics-informed Neural Networks for Solving Inverse Problems of Nonlinear Biot's Equations: Batch Training
The results show that training with small batch sizes, i. e., a few examples per batch, provides better approximations (lower percentage error) of the physical parameters than using large batches or the full batch.
Physics-informed Neural Networks for Solving Nonlinear Diffusivity and Biot's equations
This paper presents the potential of applying physics-informed neural networks for solving nonlinear multiphysics problems, which are essential to many fields such as biomedical engineering, earthquake prediction, and underground energy harvesting.
