Dr. Ehsan Haghighat is a researcher and practitioner in the areas of Scientific Machine Learning, Computational Mechanics, and Mechanics of Solids and Porous Media. His research interests include computational solid- and poro-mechanics and multiphase flow in porous media, stochastic modeling and uncertainty quantification of engineering systems, and the use of artificial intelligence tools, including deep learning, in engineering analysis. Currently, he collaborates with multiple groups on the development and application of machine learning tools for engineering applications. Additionally, he provides consulting services in these areas. For more information, please contact him through the form below.
Postdoctoral Fellow, 2020-2021
University of British Columbia
Postdoctoral Associate, 2017-2019
Massachusetts Institute of Technology
PhD in Computational Mechanics, 2011-2015
Research activities include:
Research and development activities included:
Study and research activities included:
Contact description, friction (Coulomb and Rate-and-State), and contact search.
Constitutive modeling of isotropic and anisotropic materials including nonlinear elasticity, plasticity, and heterogeneity.
Cohesive fraction propagation, localization and shear band formation, and hydraulic fracking.
A Keras wrapper for scientific computations and physics-informed deep learning using artificial neural networks