Nonlinear response history analysis (NLRHA) is generally considered to be a reliable and
robust method to assess the seismic performance of buildings under strong ground motions …

A novel data-driven constitutive modeling approach is proposed, which combines the
physics-informed nature of modeling based on continuum thermodynamics with the benefits …

This paper proposes a novel data driven scheme, called dual order-reduced Gaussian
Process emulators (DORGP), for efficiently quantifying the high-dimensional uncertain crack …

Data learning is a burgeoning area; here, we refer to it as a gorgeous blend of scientific
computing and high-dimensional statistics. In this article, we propose a generalized …

In recent years, increasingly complex computational models are being built to describe
physical systems which has led to increased use of surrogate models to reduce …

In this work we introduce a manifold learning-based surrogate modeling framework for
uncertainty quantification in high-dimensional stochastic systems. Our first goal is to perform …

Rotational head motions have been shown to play a key role in traumatic brain injury. There
is great interest in developing methods to rapidly predict brain tissue strains and strain rates …

In recent years, increasingly complex computational models are being built to describe
physical systems which has led to increased use of surrogate models to reduce …

Computational head models are promising tools for understanding and predicting traumatic
brain injuries. Most available head models are developed using inputs (ie head geometry …

Purpose To determine how the biomechanical vulnerability of the human brain is affected by
features of individual anatomy and loading. Methods To identify the features that contribute …