High-entropy alloys (HEAs) have attracted extensive interest due to their exceptional
mechanical properties and the vast compositional space for new HEAs. However …

In many situations across computational science and engineering, multiple computational
models are available that describe a system of interest. These different models have varying …

We present a deep learning framework for quantifying and propagating uncertainty in
systems governed by non-linear differential equations using physics-informed neural …

A common strategy for the dimensionality reduction of nonlinear partial differential equations
(PDEs) relies on the use of the proper orthogonal decomposition (POD) to identify a reduced …

Surrogate strategies are used widely for uncertainty quantification of groundwater models in
order to improve computational efficiency. However, their application to dynamic multiphase …

Uncertainty quantification (UQ) includes the characterization, integration, and propagation of
uncertainties that result from stochastic variations and a lack of knowledge or data in the …

A novel formulation is proposed for uncertainty propagation in risk and resilience analysis of
hierarchical systems. The main challenges are related to the complexity of hierarchical …

Here we employ and adapt the image-to-image translation concept based on conditional
generative adversarial networks (cGAN) for learning a forward and an inverse solution …

Scientific Machine Learning (SciML) and Artificial Intelligence (AI) will have broad use and
transformative effects across the Department of Energy. Accordingly, the January 2018 Basic …

We study the performance of long short-term memory networks (LSTMs) and neural ordinary
differential equations (NODEs) in learning latent-space representations of dynamical …