Hierarchical Deep Learning Neural Network (HiDeNN): An artificial intelligence (AI) framework for computational science and engineering
Published 2020 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
Hierarchical Deep Learning Neural Network (HiDeNN): An artificial intelligence (AI) framework for computational science and engineering
Authors
Keywords
Deep learning, Machine learning, Reduced order model, Data-driven discovery, Multiscale simulation, Artificial intelligence
Journal
COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
Volume 373, Issue -, Pages 113452
Publisher
Elsevier BV
Online
2020-10-21
DOI
10.1016/j.cma.2020.113452
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- MAP123-EP: A mechanistic-based data-driven approach for numerical elastoplastic analysis
- (2020) Shan Tang et al. COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
- Hierarchical deep-learning neural networks: finite elements and beyond
- (2020) Lei Zhang et al. COMPUTATIONAL MECHANICS
- Machine Learning Classical Interatomic Potentials for Molecular Dynamics from First-Principles Training Data
- (2019) Henry Chan et al. Journal of Physical Chemistry C
- Deep learning-based feature engineering methods for improved building energy prediction
- (2019) Cheng Fan et al. APPLIED ENERGY
- On-the-Fly Adaptivity for Nonlinear Twoscale Simulations Using Artificial Neural Networks and Reduced Order Modeling
- (2019) Felix Fritzen et al. Frontiers in Materials
- Derivation of heterogeneous material laws via data-driven principal component expansions
- (2019) Hang Yang et al. COMPUTATIONAL MECHANICS
- Clustering discretization methods for generation of material performance databases in machine learning and design optimization
- (2019) Hengyang Li et al. COMPUTATIONAL MECHANICS
- Self-consistent clustering analysis for multiscale modeling at finite strains
- (2019) Cheng Yu et al. COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
- Artificial intelligence for materials discovery
- (2019) Carla P. Gomes et al. MRS BULLETIN
- Data-Driven Microstructure and Microhardness Design in Additive Manufacturing Using a Self-Organizing Map
- (2019) Zhengtao Gan et al. Engineering
- Neural networks for topology optimization
- (2019) Ivan Sosnovik et al. RUSSIAN JOURNAL OF NUMERICAL ANALYSIS AND MATHEMATICAL MODELLING
- Recent advances and applications of machine learning in solid-state materials science
- (2019) Jonathan Schmidt et al. npj Computational Materials
- Bayesian Machine Learning in Metamaterial Design: Fragile Becomes Supercompressible
- (2019) Miguel A. Bessa et al. ADVANCED MATERIALS
- Data-driven discovery of coordinates and governing equations
- (2019) Kathleen Champion et al. PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
- Predictive multiscale modeling for Unidirectional Carbon Fiber Reinforced Polymers
- (2019) Jiaying Gao et al. COMPOSITES SCIENCE AND TECHNOLOGY
- An inverse modeling approach for predicting filled rubber performance
- (2019) Jiaying Gao et al. COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
- MAP123: A data-driven approach to use 1D data for 3D nonlinear elastic materials modeling
- (2019) Shan Tang 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 multi-scale multi-physics models to derive process–structure–property relationships for additive manufacturing
- (2018) Wentao Yan et al. COMPUTATIONAL MECHANICS
- Microstructural material database for self-consistent clustering analysis of elastoplastic strain softening materials
- (2018) Zeliang Liu et al. COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
- Data-Driven Mechanistic Modeling of Influence of Microstructure on High-Cycle Fatigue Life of Nickel Titanium
- (2018) Orion L. Kafka et al. JOM
- The curse(s) of dimensionality
- (2018) Naomi Altman et al. NATURE METHODS
- Machine Learning Driven Real Time Topology Optimization under Moving Morphable Component (MMC)-Based Framework
- (2018) Xin Lei et al. JOURNAL OF APPLIED MECHANICS-TRANSACTIONS OF THE ASME
- Deep learning‐Based surrogate modeling and optimization for microalgal biofuel production and photobioreactor design
- (2018) Ehecatl Antonio Rio‐Chanona et al. AICHE JOURNAL
- Data science for finite strain mechanical science of ductile materials
- (2018) Modesar Shakoor et al. COMPUTATIONAL MECHANICS
- Prediction of spinal curve progression in Adolescent Idiopathic Scoliosis using Random Forest regression
- (2018) Edgar García-Cano et al. COMPUTERS IN BIOLOGY AND MEDICINE
- Deep learning for determining a near-optimal topological design without any iteration
- (2018) Yonggyun Yu et al. STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION
- The RSNA Pediatric Bone Age Machine Learning Challenge
- (2018) Safwan S. Halabi et al. RADIOLOGY
- Why Does Deep and Cheap Learning Work So Well?
- (2017) Henry W. Lin et al. JOURNAL OF STATISTICAL PHYSICS
- Mastering the game of Go without human knowledge
- (2017) David Silver et al. NATURE
- Data-driven discovery of partial differential equations
- (2017) Samuel H. Rudy et al. Science Advances
- Data-driven computational mechanics
- (2016) T. Kirchdoerfer et al. COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
- Self-consistent clustering analysis: An efficient multi-scale scheme for inelastic heterogeneous materials
- (2016) Zeliang Liu et al. COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
- A novel deep learning approach for classification of EEG motor imagery signals
- (2016) Yousef Rezaei Tabar et al. Journal of Neural Engineering
- Discovering governing equations from data by sparse identification of nonlinear dynamical systems
- (2016) Steven L. Brunton et al. PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
- Tool life predictions in milling using spindle power with the neural network technique
- (2016) Cyril Drouillet et al. Journal of Manufacturing Processes
- Theory-Guided Machine Learning in Materials Science
- (2016) Nicholas Wagner et al. Frontiers in Materials
- Thirty years of artificial intelligence in medicine (AIME) conferences: A review of research themes
- (2015) Niels Peek et al. ARTIFICIAL INTELLIGENCE IN MEDICINE
Publish scientific posters with Peeref
Peeref publishes scientific posters from all research disciplines. Our Diamond Open Access policy means free access to content and no publication fees for authors.
Learn MoreCreate your own webinar
Interested in hosting your own webinar? Check the schedule and propose your idea to the Peeref Content Team.
Create Now