Fei Tao
Engineering · Purdue University West Lafayette
Publications
20
Citations
812
Est. group size
~3
Recurring co-author estimate
Active years
10
Publishing since 2016
Fei Tao's research focuses on combining machine learning with mechanical engineering to predict how composite materials (multi-material structures like fiber-reinforced plastics) behave and fail under stress. This work often involves coupling deep neural networks with finite element simulation software (such as Abaqus) to model material properties that are difficult to measure directly, with applications including aircraft components like rotor blades and thin-walled structures. The research sits at the intersection of structural engineering, computational mechanics, and data-driven modeling.
Publication output rose to a peak around 2020 (7 papers) after no activity in 2017-2018, then declined to about 1 publication per year from 2023 onward, suggesting a slowing pace in recent years.
Generated by claude-sonnet-5 from public bibliographic data · Jul 20, 2026
- Machine Learning Assisted Composite Rotor Blade Planform Design
2023
- Finite element coupled positive definite deep neural networks mechanics system for constitutive modeling of composites
Computer Methods in Applied Mechanics and Engineering · 2022
- Learning composite constitutive laws via coupling Abaqus and deep neural network
Composite Structures · 2021
- Learning damage constitutive law of composites via lamination theory enhanced Abaqus-PDNN mechanics system
AIAA Scitech 2021 Forum · 2021
- A neural network enhanced system for learning nonlinear constitutive law and failure initiation criterion of composites using indirectly measurable data
Composite Structures · 2020
- Learning Nonlinear Constitutive Laws Using Neural Network Models Based on Indirectly Measurable Data
Journal of Applied Mechanics · 2020
- Physics-Informed Artificial Neural Network Approach for Axial Compression Buckling Analysis of Thin-Walled Cylinder
AIAA Journal · 2020
- A neural network enhanced system for learning nonlinear constitutive relation of fiber reinforced composites
AIAA Scitech 2020 Forum · 2020
- Learning Composite Constitutive Laws Via Coupling Abaqus and Deep Neural Network
2020
- Physics-informed artificial neural network approach for axial compression buckling analysis of thin-walled cylinder
AIAA Scitech 2020 Forum · 2020
- Multiscale analysis of multilayer printed circuit board using mechanics of structure genome
Mechanics of Advanced Materials and Structures · 2019
- Composites Part B Engineering×2
- Composite Structures×2
- AIAA Scitech 2021 Forum×2
- AIAA Scitech 2020 Forum×2
- AIAA SCITECH 2022 Forum×2
- Haodong Du
Engineering · Purdue University West Lafayette
- James F. Doyle
Engineering · Purdue University West Lafayette
- Chaolin Song
Engineering · The Ohio State University
- Chulho Yang
Engineering · Purdue University West Lafayette
- Hojjat Adeli
Engineering · The Ohio State University
This profile was generated automatically from public scholarly data (OpenAlex). Group size and activity levels are estimates derived from co-authorship patterns.
Last updated Jul 20, 2026.
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