
Multifidelity classification using Gaussian processes: accelerating the prediction of largescale computational models
Machine learning techniques typically rely on large datasets to create a...
05/09/2019 ∙ by Francisco Sahli Costabal, et al. ∙ 10 ∙ shareread it

Adversarial Uncertainty Quantification in PhysicsInformed Neural Networks
We present a deep learning framework for quantifying and propagating unc...
11/09/2018 ∙ by Yibo Yang, et al. ∙ 6 ∙ shareread it

Physics Informed Deep Learning (Part II): Datadriven Discovery of Nonlinear Partial Differential Equations
We introduce physics informed neural networks  neural networks that ar...
11/28/2017 ∙ by Maziar Raissi, et al. ∙ 0 ∙ shareread it

Multistep Neural Networks for Datadriven Discovery of Nonlinear Dynamical Systems
The process of transforming observed data into predictive mathematical m...
01/04/2018 ∙ by Maziar Raissi, et al. ∙ 0 ∙ shareread it

Numerical Gaussian Processes for Timedependent and Nonlinear Partial Differential Equations
We introduce the concept of numerical Gaussian processes, which we defin...
03/29/2017 ∙ by Maziar Raissi, et al. ∙ 0 ∙ shareread it

Machine Learning of SpaceFractional Differential Equations
Datadriven discovery of "hidden physics"  i.e., machine learning of d...
08/02/2018 ∙ by Mamikon Gulian, et al. ∙ 0 ∙ shareread it

Physics Informed Deep Learning (Part I): Datadriven Solutions of Nonlinear Partial Differential Equations
We introduce physics informed neural networks  neural networks that ar...
11/28/2017 ∙ by Maziar Raissi, et al. ∙ 0 ∙ shareread it

Physicsinformed deep generative models
We consider the application of deep generative models in propagating unc...
12/09/2018 ∙ by Yibo Yang, et al. ∙ 0 ∙ shareread it

Conditional deep surrogate models for stochastic, highdimensional, and multifidelity systems
We present a probabilistic deep learning methodology that enables the co...
01/15/2019 ∙ by Yibo Yang, et al. ∙ 0 ∙ shareread it

PhysicsConstrained Deep Learning for Highdimensional Surrogate Modeling and Uncertainty Quantification without Labeled Data
Surrogate modeling and uncertainty quantification tasks for PDE systems ...
01/18/2019 ∙ by Yinhao Zhu, et al. ∙ 0 ∙ shareread it

Machine learning in cardiovascular flows modeling: Predicting pulse wave propagation from noninvasive clinical measurements using physicsinformed deep learning
Advances in computational science offer a principled pipeline for predic...
05/13/2019 ∙ by Georgios Kissas, et al. ∙ 0 ∙ shareread it

A comparative study of physicsinformed neural network models for learning unknown dynamics and constitutive relations
We investigate the use of discrete and continuous versions of physicsin...
04/02/2019 ∙ by Ramakrishna Tipireddy, et al. ∙ 0 ∙ shareread it
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