Timothy Michael Wildey

Scientific Machine Learning

Author profile picture

Scientific Machine Learning



(505) 844-0760

Sandia National Laboratories, New Mexico
P.O. Box 5800
Albuquerque, NM 87185-1318


Tim joined Sandia National Labs in January, 2011 following a postdoctoral fellowship at the University of Texas at Austin. His research interests are finite element and finite volume methods, discontinuous Galerkin methods, hybridized discretizations, a posteriori error analysis and estimation, uncertainty quantification, adjoint methods, multiphysics and multiscale problems, operator splitting and decomposition, computational fluid dynamics, shock-hydrodynamics, geomechanics, flow and transport in porous media, numerical linear algebra, domain decomposition, multilevel and multiscale preconditioners, and parallel computing.



Anh Tran, Timothy Michael Wildey, Hojun Lim, (2022). Microstructure-Sensitive UQ for Materials Constitutive Models in Crystal Plasticity Finite Element Method USACM Thematic Conference on Uncertainty Quantification for Machine Learning Integrated Physics Modeling (UQ-MLIP) Document ID: 1595518

Bryan William Reuter, Timothy Michael Wildey, (2022). Synchronous and Asynchronous Time Integration for Multiscale Simulations Using Hybridized Finite Element Methods 15th World Congress on Computational Mechanics Document ID: 1573853

Tian Yu NMN Yen, Timothy Michael Wildey, (2022). Using Manifold Learning to Enable Computationally Efficient Stochastic Inversion with High-dimensional Data Wccm-apcom 2022 Document ID: 1573846

Timothy Michael Wildey, Gianluca Geraci, Michael S. Eldred, John Davis Jakeman, Owen Davis, Teresa Portone, Tian Yu NMN Yen, Bryan William Reuter, Alex Gorodetsky, Ahmad Rushdi, Daniele Schiavazzi, Lauren Partin, (2022). Embedded uncertainty estimation for data-driven surrogates to enable trustworthy ML for UQ Machine Learning and Deep Learning Conference Document ID: 1573362

Anh Tran, Timothy Michael Wildey, Hojun Lim, (2022). Microstructure-sensitive uncertainty quantification for crystal plasticity finite element constitutive models using stochastic collocation methods Frontiers in Materials Document ID: 1540494

Showing Results. Show More Publications