9 papers
Numerical Considerations for the Construction of Karhunen-Loève Expansions
Cosmin Safta, Habib N. Najm
This report examines numerical aspects of constructing Karhunen-Loève expansions (KLEs) for second-order stochastic processes. The KLE relies on the spectral decomposition of the…
Towards Spatio-Temporal Extrapolation of Phase-Field Simulations with Convolution-Only Neural Networks
Christophe Bonneville, Nathan Bieberdorf, Pieterjan Robbe +4
Phase-field simulations of liquid metal dealloying (LMD) can capture complex microstructural evolutions but can be prohibitively expensive for large domains and long time horizons.…
Bifidelity Karhunen-Loève Expansion Surrogate with Active Learning for Random Fields
Aniket Jivani, Cosmin Safta, Beckett Y. Zhou +1
We present a bifidelity Karhunen-Loève expansion (KLE) surrogate model for field-valued quantities of interest (QoIs) under uncertain inputs. The approach combines the spectral ef…
Extrapolating Phase-Field Simulations in Space and Time with Purely Convolutional Architectures
Christophe Bonneville, Nathan Bieberdorf, Pieterjan Robbe +4
Phase-field models of liquid metal dealloying (LMD) can resolve rich microstructural dynamics but become intractable for large domains or long time horizons. We present a condition…
A Comparison of Surrogate Constitutive Models for Viscoplastic Creep Simulation of HT-9 Steel
Pieterjan Robbe, Andre Ruybalid, Arun Hegde +4
Mechanistic microstructure-informed constitutive models for the mechanical response of polycrystals are a cornerstone of computational materials science. However, as these models b…
Uncertainty quantification of neural network models of evolving processes via Langevin sampling
Cosmin Safta, Reese E. Jones, Ravi G. Patel +4
We propose a scalable, approximate inference hypernetwork framework for a general model of history-dependent processes. The flexible data model is based on a neural ordinary differ…