
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

Algorithmic Linearly Constrained Gaussian Processes
We algorithmically construct multioutput Gaussian process priors which ...
01/28/2018 ∙ by Markus LangeHegermann, et al. ∙ 0 ∙ shareread it

Hidden Physics Models: Machine Learning of Nonlinear Partial Differential Equations
While there is currently a lot of enthusiasm about "big data", useful da...
08/02/2017 ∙ 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

Deeplearning PDEs with unlabeled data and hardwiring physics laws
Providing fast and accurate solutions to partial differential equations ...
04/13/2019 ∙ by S. Mohammad H. Hashemi, et al. ∙ 0 ∙ shareread it

PDENet 2.0: Learning PDEs from Data with A NumericSymbolic Hybrid Deep Network
Partial differential equations (PDEs) are commonly derived based on empi...
11/30/2018 ∙ by Zichao Long, et al. ∙ 0 ∙ shareread it

Jets and differential linear logic
We prove that the category of vector bundles over a fixed smooth manifol...
11/15/2018 ∙ by James Wallbridge, et al. ∙ 0 ∙ shareread it
Machine Learning of Linear Differential Equations using Gaussian Processes
This work leverages recent advances in probabilistic machine learning to discover conservation laws expressed by parametric linear equations. Such equations involve, but are not limited to, ordinary and partial differential, integrodifferential, and fractional order operators. Here, Gaussian process priors are modified according to the particular form of such operators and are employed to infer parameters of the linear equations from scarce and possibly noisy observations. Such observations may come from experiments or "blackbox" computer simulations.
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