Deep learning of thermodynamics-aware reduced-order models from data

Physically sound, self-learning digital twins for sloshing fluids

(2020) Beatriz Moya et al.   PLoS One

An Augmented Reality platform for interactive aerodynamic design and analysis

(2019) Alberto Badías et al.   INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN ENGINEERING

Learning slosh dynamics by means of data

(2019) B. Moya et al.   COMPUTATIONAL MECHANICS

Machine learning materials physics: Integrable deep neural networks enable scale bridging by learning free energy functions

(2019) G.H. Teichert et al.   COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING

On learning Hamiltonian systems from data

(2019) Tom Bertalan et al.   CHAOS

Model reduction of dynamical systems on nonlinear manifolds using deep convolutional autoencoders

(2019) Kookjin Lee et al.   JOURNAL OF COMPUTATIONAL PHYSICS

Thermodynamically consistent data-driven computational mechanics

(2018) David González et al.   CONTINUUM MECHANICS AND THERMODYNAMICS

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

Testing the manifold hypothesis

(2016) Charles Fefferman et al.   JOURNAL OF THE AMERICAN MATHEMATICAL SOCIETY

Deep learning in neural networks: An overview

(2015) Jürgen Schmidhuber   NEURAL NETWORKS

Preservation of thermodynamic structure in model reduction

(2015) Hans Christian Öttinger   PHYSICAL REVIEW E

POD reduced-order unstructured mesh modeling applied to 2D and 3D fluid flow

(2012) J. Du et al.   COMPUTERS & MATHEMATICS WITH APPLICATIONS

Conservative interpolation between volume meshes by local Galerkin projection

(2010) P.E. Farrell et al.   COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING

Real-time deformable models of non-linear tissues by model reduction techniques

(2008) S. Niroomandi et al.   COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE