Surrogate neural network model for sensitivity analysis and uncertainty quantification of the mechanical behavior in the optical lens-barrel assembly

Meshless Physics‐Informed Deep Learning Method for Three‐Dimensional Solid Mechanics

(2021) Diab W. Abueidda et al.   INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN ENGINEERING

Bayesian neural networks for uncertainty quantification in data-driven materials modeling

(2021) Audrey Olivier et al.   COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING

Optimization of solidification in die casting using numerical simulations and machine learning

(2020) Shantanu Shahane et al.   Journal of Manufacturing Processes

Topology optimization of 2D structures with nonlinearities using deep learning

(2020) Diab W. Abueidda et al.   COMPUTERS & STRUCTURES

ChemNet: A Deep Neural Network for Advanced Composites Manufacturing

(2020) Elyas Goli et al.   JOURNAL OF PHYSICAL CHEMISTRY B

Solving Partial Differential Equations Using Deep Learning and Physical Constraints

(2020) Yanan Guo et al.   Applied Sciences-Basel

Wettability-defined frosting dynamics between plane fins in quiescent air

(2020) Kazi Fazle Rabbi et al.   INTERNATIONAL JOURNAL OF HEAT AND MASS TRANSFER

Deep learning for plasticity and thermo-viscoplasticity

(2020) Diab W. Abueidda et al.   INTERNATIONAL JOURNAL OF PLASTICITY

Deep learning electromagnetic inversion with convolutional neural networks

(2019) Vladimir Puzyrev   GEOPHYSICAL JOURNAL INTERNATIONAL

Uncertainty quantification in three dimensional natural convection using polynomial chaos expansion and deep neural networks

(2019) Shantanu Shahane et al.   INTERNATIONAL JOURNAL OF HEAT AND MASS TRANSFER

Finite volume simulation framework for die casting with uncertainty quantification

(2019) Shantanu Shahane et al.   APPLIED MATHEMATICAL MODELLING

Predicting the effective thermal conductivity of composites from cross sections images using deep learning methods

(2019) Qingyuan Rong et al.   COMPOSITES SCIENCE AND TECHNOLOGY

Machine learning aided stochastic elastoplastic analysis

(2019) Yuan Feng et al.   COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING

Deep learning predicts path-dependent plasticity

(2019) M. Mozaffar et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

Data-Driven Materials Investigations: The Next Frontier in Understanding and Predicting Fatigue Behavior

(2018) Ashley D. Spear et al.   JOM

Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations

(2018) M. Raissi et al.   JOURNAL OF COMPUTATIONAL PHYSICS

Impact of geometric uncertainty on hemodynamic simulations using machine learning

(2015) Sethuraman Sankaran et al.   COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING