Transfer learning as a method to reproduce high-fidelity non-local thermodynamic equilibrium opacities in simulations

Physics-informed Deep Neural Network for Inhomogeneous Magnetized Plasma Parameter Inversion

(2022) Yangyang Zhang et al.   IEEE Antennas and Wireless Propagation Letters

Magnetic control of tokamak plasmas through deep reinforcement learning

(2022) Jonas Degrave et al.   NATURE

Cognitive simulation models for inertial confinement fusion: Combining simulation and experimental data

(2021) K. D. Humbird et al.   PHYSICS OF PLASMAS

A Self-Adaptive Mutation Neural Architecture Search Algorithm Based on Blocks

(2021) Yu Xue et al.   IEEE Computational Intelligence Magazine

A Multi-Objective Evolutionary Approach Based on Graph-in-Graph for Neural Architecture Search of Convolutional Neural Networks

(2021) Yu Xue et al.   International Journal of Neural Systems

Evolutionary Neural Architecture Search for High-Dimensional Skip-Connection Structures on DenseNet Style Networks

(2021) Damien O'Neill et al.   IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION

A fast and accurate physics-informed neural network reduced order model with shallow masked autoencoder

(2021) Youngkyu Kim et al.   JOURNAL OF COMPUTATIONAL PHYSICS

Conservative physics-informed neural networks on discrete domains for conservation laws: Applications to forward and inverse problems

(2020) Ameya D. Jagtap et al.   COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING

Transfer learning based multi-fidelity physics informed deep neural network

(2020) Souvik Chakraborty   JOURNAL OF COMPUTATIONAL PHYSICS

A Comprehensive Survey on Transfer Learning

(2020) Fuzhen Zhuang et al.   PROCEEDINGS OF THE IEEE

Towards calibration-invariant spectroscopy using deep learning

(2019) M. Chatzidakis et al.   Scientific Reports

Adversarial uncertainty quantification in physics-informed neural networks

(2019) Yibo Yang et al.   JOURNAL OF COMPUTATIONAL PHYSICS

General Multi-Fidelity Framework for Training Artificial Neural Networks With Computational Models

(2019) Roland Can Aydin et al.   Frontiers in Materials

Feature selection based convolutional neural network pruning and its application in calibration modeling for NIR spectroscopy

(2019) Yuan-yuan Chen et al.   CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS

Transfer learning with deep manifold regularized auto-encoders

(2019) Yi Zhu et al.   NEUROCOMPUTING

Transfer Learning as a Tool for Reducing Simulation Bias: Application to Inertial Confinement Fusion

(2019) Bogdan Kustowski et al.   IEEE TRANSACTIONS ON PLASMA SCIENCE

Transfer Learning to Model Inertial Confinement Fusion Experiments

(2019) K. D. Humbird et al.   IEEE TRANSACTIONS ON PLASMA SCIENCE

A composite neural network that learns from multi-fidelity data: Application to function approximation and inverse PDE problems

(2019) Xuhui Meng et al.   JOURNAL OF COMPUTATIONAL PHYSICS

Physics Informed Extreme Learning Machine (PIELM)–A rapid method for the numerical solution of partial differential equations

(2019) Vikas Dwivedi et al.   NEUROCOMPUTING

Transfer learning enhanced physics informed neural network for phase-field modeling of fracture

(2019) Somdatta Goswami et al.   THEORETICAL AND APPLIED FRACTURE MECHANICS

Modern practical convolutional neural networks for multivariate regression: Applications to NIR calibration

(2018) Chenhao Cui et al.   CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS

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

Deep Neural Network Initialization With Decision Trees

(2018) Kelli D. Humbird et al.   IEEE Transactions on Neural Networks and Learning Systems