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Found 6 papers, 1 papers with code
Sensitivity Analysis in the Presence of Intrinsic Stochasticity for Discrete Fracture Network Simulations
In this paper, we perform a Sensitivity Analysis, which directly attributes the uncertainty observed in the QoI to the epistemic uncertainty from each input parameter and to the aleatoric uncertainty.
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.
Predictive Scale-Bridging Simulations through Active Learning
Throughout computational science, there is a growing need to utilize the continual improvements in raw computational horsepower to achieve greater physical fidelity through scale-bridging over brute-force increases in the number of mesh elements.
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).
Learning to fail: Predicting fracture evolution in brittle material models using recurrent graph convolutional neural networks
Our methods use deep learning and train on simulation data from high-fidelity models, emulating the results of these models while avoiding the overwhelming computational demands associated with running a statistically significant sample of simulations.
Machine learning for graph-based representations of three-dimensional discrete fracture networks
Restricting the flowing fracture network to this backbone provides a significant reduction in the network's effective size.
