Iterative surrogate model optimization (ISMO): An active learning algorithm for PDE constrained optimization with deep neural networks

SciPy 1.0: fundamental algorithms for scientific computing in Python

(2020) Pauli Virtanen et al.   NATURE METHODS

A multi-level procedure for enhancing accuracy of machine learning algorithms

(2020) KJETIL O. LYE et al.   EUROPEAN JOURNAL OF APPLIED MATHEMATICS

Deep learning observables in computational fluid dynamics

(2020) Kjetil O. Lye et al.   JOURNAL OF COMPUTATIONAL PHYSICS

An artificial neural network as a troubled-cell indicator

(2018) Deep Ray et al.   JOURNAL OF COMPUTATIONAL PHYSICS

Hidden physics models: Machine learning of nonlinear partial differential equations

(2018) Maziar Raissi 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

Aerodynamic Shape Optimization Investigations of the Common Research Model Wing Benchmark

(2015) Zhoujie Lyu et al.   AIAA JOURNAL

Deep learning

(2015) Yann LeCun et al.   NATURE

Kriging-based optimization applied to flow control

(2011) R. Duvigneau et al.   INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN FLUIDS

Efficient shape optimization for certain and uncertain aerodynamic design

(2010) Claudia Schillings et al.   COMPUTERS & FLUIDS