no code implementations • 7 Dec 2023 • Wyatt Bridgman, Uma Balakrishnan, Reese Jones, Jiefu Chen, Xuqing Wu, Cosmin Safta, Yueqin Huang, Mohammad Khalil
For black-box simulations, non-intrusive PCE allows the construction of these surrogates using a set of simulation response evaluations.
no code implementations • 12 Jul 2023 • Wyatt Bridgman, Reese Jones, Mohammad Khalil
In this work, we propose a method for constructing an initial Gaussian mixture model approximation that can be used to warm-start the iterative solvers for variational inference.
no code implementations • 15 Oct 2022 • Jan N. Fuhg, Craig M. Hamel, Kyle Johnson, Reese Jones, Nikolaos Bouklas
The development of accurate constitutive models for materials that undergo path-dependent processes continues to be a complex challenge in computational solid mechanics.
no code implementations • 29 Sep 2022 • Reese Jones, Cosmin Safta, Ari Frankel
We develop a means of deep learning of hidden features on the reduced graph given the native discretization and a segmentation of the initial input field.
no code implementations • 31 Jan 2022 • Wyatt Bridgman, Xiaoxuan Zhang, Greg Teichert, Mohammad Khalil, Krishna Garikipati, Reese Jones
In this work we employ an encoder-decoder convolutional neural network to predict the failure locations of porous metal tension specimens based only on their initial porosities.
no code implementations • 4 Jun 2021 • Ari Frankel, Cosmin Safta, Coleman Alleman, Reese Jones
Predicting the evolution of a representative sample of a material with microstructure is a fundamental problem in homogenization.
no code implementations • 23 Dec 2019 • Ari Frankel, Reese Jones, Laura Swiler
Finally, we consider an approach that recovers the strain-energy density function and derives the stress tensor from this potential.