The objective of this work is to describe the design and implementation of a framework for
building multi-disciplinary finite element programs. The main goals are generality …

Regression is an important branch of engineering data mining tasks, aiming to establish a
regression model to predict the output of interest based on the input variables. To meet the …

The present paper describes a method to enhance the capability of, or to broaden the scope
of computational mechanics by using deep learning, which is one of the machine learning …

Physics Informed Neural Network (PINN) is a scientific computing framework used to solve
both forward and inverse problems modeled by Partial Differential Equations (PDEs). This …

Multivariate adaptive regression splines (MARS) is a statistical modeling approach with wide-
ranging real-world applications. In the MARS model building process, knot positioning is a …

Optimizing the parameters of partial differential equations (PDEs), ie, PDE-constrained
optimization (PDE-CO), allows us to model natural systems from observations or perform …

Microseismic monitoring is an increasingly common geophysical tool to monitor the changes
in the subsurface. Autopicking involving phase arrival detection is a common element in …

In this study, bio-inspired computational intelligence is exploited to analyze the nonlinear
vibrational dynamics of rotating electrical machine (VD-REM) model by applying artificial …

Predictive models that accurately emulate complex scientific processes can achieve speed-
ups over numerical simulators or experiments and at the same time provide surrogates for …

HUGO [1] and MOD [2] are the most secure adaptive embedding algorithms of 2011. These
algorithms strive to hide a secret message, while minimizing an ad hoc embedding impact …