Support vector machine (SVM) is a powerful machine learning technique relying on the
structural risk minimization principle. The applications of SVM in structural reliability analysis …

Building energy prediction plays a vital role in develo** a model predictive controller for
consumers and optimizing energy distribution plan for utilities. Common approaches for …

This paper introduces an innovative physics-informed deep learning framework for
metamodeling of nonlinear structural systems with scarce data. The basic concept is to …

The main and pivot part of electric companies is the load forecasting. Decision-makers and
think tank of power sectors should forecast the future need of electricity with large accuracy …

The idea of using a simpler surrogate to represent a complex phenomenon has gained
increasing popularity over past three decades. Due to their ability to exploit the black-box …

In simulation-based realization of complex systems, we are forced to address the issue of
computational complexity. One critical issue that must be addressed is the approximation of …

Accurate prediction of building's response subjected to earthquakes makes possible to
evaluate building performance. To this end, we leverage the recent advances in deep …

Electricity load forecasting is an important tool which can be utilized to enable effective
control of commercial building electricity loads. Accurate forecasts of commercial building …

Metamodeling is becoming a rather popular means to approximate the expensive
simulations in today's complex engineering design problems since accurate metamodels …

Measuring variable importance for computational models or measured data is an important
task in many applications. It has drawn our attention that the variable importance analysis …