no code implementations • 7 Dec 2023 • Alexander C. Murph, Justin D. Strait, Kelly R. Moran, Jeffrey D. Hyman, Hari S. Viswanathan, Philip H. Stauffer
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.
no code implementations • 4 Oct 2023 • Teeratorn Kadeethum, Daniel O'Malley, Youngsoo Choi, Hari S. Viswanathan, Hongkyu Yoon
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.
no code implementations • 20 Sep 2022 • Satish Karra, Mohamed Mehana, Nicholas Lubbers, Yu Chen, Abdourahmane Diaw, Javier E. Santos, Aleksandra Pachalieva, Robert S. Pavel, Jeffrey R. Haack, Michael McKerns, Christoph Junghans, Qinjun Kang, Daniel Livescu, Timothy C. Germann, Hari S. Viswanathan
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.
1 code implementation • 27 May 2021 • Teeratorn Kadeethum, Daniel O'Malley, Jan Niklas Fuhg, Youngsoo Choi, Jonghyun Lee, Hari S. Viswanathan, Nikolaos Bouklas
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).
no code implementations • 14 Oct 2018 • Max Schwarzer, Bryce Rogan, Yadong Ruan, Zhengming Song, Diana Y. Lee, Allon G. Percus, Viet T. Chau, Bryan A. Moore, Esteban Rougier, Hari S. Viswanathan, Gowri Srinivasan
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.
no code implementations • 27 May 2017 • Manuel Valera, Zhengyang Guo, Priscilla Kelly, Sean Matz, Vito Adrian Cantu, Allon G. Percus, Jeffrey D. Hyman, Gowri Srinivasan, Hari S. Viswanathan
Restricting the flowing fracture network to this backbone provides a significant reduction in the network's effective size.