no code implementations • 19 Apr 2022 • Amanda A. Howard, Mauro Perego, George E. Karniadakis, Panos Stinis
We demonstrate the new multi-fidelity training in diverse examples, including modeling of the ice-sheet dynamics of the Humboldt glacier, Greenland, using two different fidelity models and also using the same physical model at two different resolutions.
no code implementations • 8 Apr 2020 • Guofei Pang, Marta D'Elia, Michael Parks, George E. Karniadakis
In this paper, we extend PINNs to parameter and function inference for integral equations such as nonlocal Poisson and nonlocal turbulence models, and we refer to them as nonlocal PINNs (nPINNs).
6 code implementations • 10 Jul 2019 • Lu Lu, Xuhui Meng, Zhiping Mao, George E. Karniadakis
We also present a Python library for PINNs, DeepXDE, which is designed to serve both as an education tool to be used in the classroom as well as a research tool for solving problems in computational science and engineering.
no code implementations • 16 Dec 2018 • Seungjoon Lee, Felix Dietrich, George E. Karniadakis, Ioannis G. Kevrekidis
In this paper, we will explore mathematical algorithms for multifidelity information fusion that use such an approach towards improving the representation of the high-fidelity function with only a few training data points.
2 code implementations • 18 Nov 2016 • Ansel L. Blumers, Yu-Hang Tang, Zhen Li, Xuejin Li, George E. Karniadakis
We observe a speedup of 10. 1 on one GPU over all 16 cores within a single node, and a weak scaling efficiency of 91% across 256 nodes.
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