Physics-informed neural networks for PDE-constrained optimization and control
| dc.contributor.author | Barry-Straume, Jostein | en |
| dc.contributor.author | Sarshar, Arash | en |
| dc.contributor.author | Popov, Andrey A. | en |
| dc.contributor.author | Sandu, Adrian | en |
| dc.date.accessioned | 2022-05-07T19:05:22Z | en |
| dc.date.available | 2022-05-07T19:05:22Z | en |
| dc.date.issued | 2022-05-06 | en |
| dc.description.abstract | A fundamental problem of science is designing optimal control policies that manipulate a given environment into producing the desired outcome. Control PhysicsInformed Neural Networks simultaneously solve a given system state, and its respective optimal control, in a one-stage framework that conforms to the physical laws of the system. Prior approaches use a two-stage framework that models and controls a system sequentially, whereas Control PINNs incorporate the required optimality conditions in their architecture and loss function. The success of Control PINNs is demonstrated by solving the following open-loop optimal control problems: (i) an analytical problem (ii) a one-dimensional heat equation, and (iii) a two-dimensional predator-prey problem. | en |
| dc.identifier.uri | http://hdl.handle.net/10919/109822 | en |
| dc.language.iso | en | en |
| dc.relation.ispartofseries | ;CSL-TR-22-2 | en |
| dc.title | Physics-informed neural networks for PDE-constrained optimization and control | en |
| dc.type | Article | en |
| dc.type.dcmitype | Text | en |