A physics-guided neural network framework for elastic plates: Comparison of governing equations-based and energy-based approaches

Lift & Learn: Physics-informed machine learning for large-scale nonlinear dynamical systems

(2020) Elizabeth Qian et al.   PHYSICA D-NONLINEAR PHENOMENA

An energy approach to the solution of partial differential equations in computational mechanics via machine learning: Concepts, implementation and applications

(2020) E. Samaniego et al.   COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING

A machine learning based plasticity model using proper orthogonal decomposition

(2020) Dengpeng Huang et al.   COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING

A recurrent neural network-accelerated multi-scale model for elasto-plastic heterogeneous materials subjected to random cyclic and non-proportional loading paths

(2020) Ling Wu et al.   COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING

Data-driven prediction of battery cycle life before capacity degradation

(2019) Kristen A. Severson et al.   Nature Energy

A mesh-free method for interface problems using the deep learning approach

(2019) Zhongjian Wang et al.   JOURNAL OF COMPUTATIONAL PHYSICS

PDE-Net 2.0: Learning PDEs from data with a numeric-symbolic hybrid deep network

(2019) Zichao Long et al.   JOURNAL OF COMPUTATIONAL PHYSICS

Machine learning in chemoinformatics and drug discovery

(2018) Yu-Chen Lo et al.   DRUG DISCOVERY TODAY

Gradient-enhanced kriging for high-dimensional problems

(2018) Mohamed A. Bouhlel et al.   ENGINEERING WITH COMPUTERS

Stretch-induced wrinkling of highly orthotropic thin films

(2018) Juner Zhu et al.   INTERNATIONAL JOURNAL OF SOLIDS AND STRUCTURES

DGM: A deep learning algorithm for solving partial differential equations

(2018) Justin Sirignano et al.   JOURNAL OF COMPUTATIONAL PHYSICS

Solving high-dimensional partial differential equations using deep learning

(2018) Jiequn Han et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

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

Theory-Guided Data Science: A New Paradigm for Scientific Discovery from Data

(2017) Anuj Karpatne et al.   IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING

Deep Convolutional Neural Networks for Image Classification: A Comprehensive Review

(2017) Waseem Rawat et al.   NEURAL COMPUTATION

Physics-informed machine learning approach for reconstructing Reynolds stress modeling discrepancies based on DNS data

(2017) Jian-Xun Wang et al.   Physical Review Fluids

Machine learning in materials informatics: recent applications and prospects

(2017) Rampi Ramprasad et al.   npj Computational Materials

Disappearance of stretch-induced wrinkles of thin sheets: A study of orthotropic films

(2016) András A. Sipos et al.   INTERNATIONAL JOURNAL OF SOLIDS AND STRUCTURES

Machine learning applications in genetics and genomics

(2015) Maxwell W. Libbrecht et al.   NATURE REVIEWS GENETICS

Stretch-induced stress patterns and wrinkles in hyperelastic thin sheets

(2011) Vishal Nayyar et al.   INTERNATIONAL JOURNAL OF SOLIDS AND STRUCTURES