Journal
JOURNAL OF COMPUTATIONAL PHYSICS
Volume 348, Issue -, Pages 683-693Publisher
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.jcp.2017.07.050
Keywords
Probabilistic machine learning; Inverse problems; Fractional differential equations; Uncertainty quantification; Functional genomics
Funding
- DARPA EQUiPS grant [N66001-15-2-4055]
- MURI/ARO grant [W911NF-15-1-0562]
- AFOSR grant [FA9550-17-1-0013]
Ask authors/readers for more resources
This work leverages recent advances in probabilistic machine learning to discover governing equations expressed by parametric linear operators. Such equations involve, but are not limited to, ordinary and partial differential, integro-differential, 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, as demonstrated in several synthetic examples and a realistic application in functional genomics. (C) 2017 Elsevier Inc. All rights reserved.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available