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About 1805 07297 PDF
This document proposes using deep reinforcement learning to solve nonlinear differential equations. A deep neural network actor outputs candidate solutions, and a critic derived from the governing equations, boundary/initial conditions evaluates the solutions. Solutions are treated as separate tasks that can benefit from transfer learning between time steps. The approach is validated on several example equations and shows high accuracy solutions with faster computation compared to traditional methods.
Detailed Information
| Author: | ['Tarek Hajj Shehadi'] |
|---|---|
| Publication Year: | 2020 |
| Pages: | 29 |
| Language: | English |
| Format: | |
| Price: | FREE |
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