My research interests are data-driven computational mechanics, uncertainty quantification and robust topology optimization.  Focusing on speeding up computation analysis of complex mechanics systems, I developed efficient and accurate surrogate models and computational frameworks in various mechanics applications such as forward stochastic problems, multiscale analysis, and inverse robust design optimization considering uncertainties.

• Nan Feng, Kapil Khandelwal, Seth Watts, Daniel Tortorelli, 3D finite strain data-driven nonlinear homogenization based on machine learning models for periodic metamaterials.

Manuscript ready

• Nan Feng, Kapil Khandelwal, Seth Watts, Daniel Tortorelli, Nonlinear homogenization and stability analysis of Schwarz periodic metamaterials, Extreme Mechanics Letters.

• Nan Feng, Guodong Zhang, Kapil Khandelwal, Finite strain robust topology optimization considering multiple uncertain sources, International Journal for Numerical Methods in Engineering.

Published

• Guodong Zhang, Nan Feng, Kapil Khandelwal, A computational framework for homogenization and multiscale stability analyses of nonlinear periodic materials, International Journal for Numerical Methods in Engineering, (2021). [DOI]

• Nan Feng, Guodong Zhang, Kapil Khandelwal, Finite strain FE2 analysis with data-driven homogenization using deep neural networks, Computers & Structures, (2022). [DOI]

• Nan Feng, Guodong Zhang, Kapil Khandelwal, On the Application of Data-Driven Deep Neural Networks in Linear and Nonlinear Structural Dynamics, arXiv , (2021). [DOI]

• Nan Feng, Guodong Zhang, Kapil Khandelwal, On the performance evaluation of stochastic finite elements in linear and nonlinear problems, Computers & Structures, (2021). [DOI]