Related references
Note: Only part of the references are listed.Model reduction of dynamical systems on nonlinear manifolds using deep convolutional autoencoders
Kookjin Lee et al.
JOURNAL OF COMPUTATIONAL PHYSICS (2020)
Physically sound, self-learning digital twins for sloshing fluids
Beatriz Moya et al.
PLOS ONE (2020)
Searching for new physics with deep autoencoders
Marco Farina et al.
PHYSICAL REVIEW D (2020)
Thermodynamically consistent data-driven computational mechanics
David Gonzalez et al.
CONTINUUM MECHANICS AND THERMODYNAMICS (2019)
Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
M. Raissi et al.
JOURNAL OF COMPUTATIONAL PHYSICS (2019)
An augmented reality platform for interactive aerodynamic design and analysis
Alberto Badias et al.
INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN ENGINEERING (2019)
Learning slosh dynamics by means of data
B. Moya et al.
COMPUTATIONAL MECHANICS (2019)
Machine learning materials physics: Integrable deep neural networks enable scale bridging by learning free energy functions
G. H. Teichert et al.
COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING (2019)
On learning Hamiltonian systems from data
Tom Bertalan et al.
CHAOS (2019)
Consistent Data-Driven Computational Mechanics
D. Gonzalez et al.
PROCEEDINGS OF 21ST INTERNATIONAL ESAFORM CONFERENCE ON MATERIAL FORMING (ESAFORM 2018) (2018)
DeepToF: Off-the-Shelf Real-Time Correction of Multipath Interference in Time-of-Flight Imaging
Julio Marco et al.
ACM TRANSACTIONS ON GRAPHICS (2017)
A Proposal on Machine Learning via Dynamical Systems
E. Weinan
COMMUNICATIONS IN MATHEMATICS AND STATISTICS (2017)
TESTING THE MANIFOLD HYPOTHESIS
Charles Fefferman et al.
JOURNAL OF THE AMERICAN MATHEMATICAL SOCIETY (2016)
Deep learning in neural networks: An overview
Juergen Schmidhuber
NEURAL NETWORKS (2015)
Preservation of thermodynamic structure in model reduction
Hans Christian Oettinger
PHYSICAL REVIEW E (2015)
POD reduced-order unstructured mesh modeling applied to 2D and 3D fluid flow
J. Du et al.
COMPUTERS & MATHEMATICS WITH APPLICATIONS (2013)
Conservative interpolation between volume meshes by local Galerkin projection
P. E. Farrell et al.
COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING (2011)
Real-time deformable models of non-linear tissues by model reduction techniques
S. Niroomandi et al.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE (2008)
Model reduction for compressible flows using POD and Galerkin projection
CW Rowley et al.
PHYSICA D-NONLINEAR PHENOMENA (2004)
Reliable real-time solution of parametrized partial differential equations: Reduced-basis output bound methods
C Prud'homme et al.
JOURNAL OF FLUIDS ENGINEERING-TRANSACTIONS OF THE ASME (2002)