Name
Abstract | ||
|---|---|---|
There are two types of ordinary differential equations (ODEs): initial value problems (IVPs) and boundary value problems (BVPs). While many probabilistic numerical methods for the solution of IVPs have been presented to-date, there exists no efficient probabilistic general-purpose solver for nonlinear BVPs. Our method based on iterated Gaussian process (GP) regression returns a GP posterior over the solution of nonlinear ODEs, which provides a meaningful error estimation via its predictive posterior standard deviation. Our solver is fast (typically of quadratic convergence rate) and the theory of convergence can be transferred from prior non-probabilistic work. Our method performs on par with standard codes for an established benchmark of test problems. |
Year | Venue | Field |
|---|---|---|
2019 | international conference on machine learning | Applied mathematics,Off the shelf,Pattern recognition,Ordinary differential equation,Computer science,Gaussian,Artificial intelligence,Solver |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| David John | 1 | 0 | 0.34 |
| Vincent Heuveline | 2 | 179 | 30.51 |
| Michael Schober | 3 | 12 | 2.29 |