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Date | Oct 23, 2024 |
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Speaker | 한지훈 |

Dept. | Dartmouth College) |

Room | 27-220 |

Time | 15:00-17:30 |

I will discuss the derivative-free loss method (DFLM), a stochastic approach for solving PDEs. The method uses the stochastic representation of the PDE in the spirit of the Feynman-Kac formula. It characterizes the averaging of collective information from stochastic walkers’ paths exploring the neighborhood of a point of interest. While exploring the domain with an iterative averaging process, a neural network is reinforced to approximate the PDE solution. I will cover its analysis regarding trainability and highlight its effectiveness in non-intrusively tackling multiscale problems with highly oscillating coefficients and perforated domain problems.

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