Data-driven Predictive Framework for Modeling Complex Multi-physics Engineering Applications

Arturo Schiaffino Bustamante, University of Texas at El Paso


Computational models are often encountered in multiple engineering application, such as structural design, material science, heat transfer and fluid dynamics. These simulations offer the engineers the capability of understanding complex physical situations before putting them to practice, either through experimentation or prototyping. The current advances in computational sciences, hardware architecture, software development and big data technology, have allowed the construction of sturdy predicting frameworks for analyzing a wide array of natural phenomena across different disciplines, either through the implementation of statistical methods, such as big data, and uncertainty quantification, or through high performance computing of a numerical model. The objective of this work is to study the implementation of a parallel, exa-scale pore network model based on Trilinos and Dakota, software packages developed by Sandia National Labs, along with machine learning techniques programmed by using TensorFlow, which are configured to predict complex multi-physics phenomena, such as flow through porous media or coolant explosions inside of industrial furnaces. Several predictions were made by using deep neural networks and uncertainty quantification of big data, proving that there is definitely a research window with big data in the Mechanical Engineering Sciences.

Subject Area

Applied Mathematics|Mechanical engineering

Recommended Citation

Bustamante, Arturo Schiaffino, "Data-driven Predictive Framework for Modeling Complex Multi-physics Engineering Applications" (2018). ETD Collection for University of Texas, El Paso. AAI13425410.