A DEEP LEARNING APPROACH FOR RARE EVENT SIMULATION IN DIFFUSION PROCESSES
Paper i proceeding, 2024

We address the challenge of estimating rare events associated with stochastic differential equations using importance sampling. The importance sampling zero variance measure in these settings can be inferred from a solution to the Hamilton-Jacobi-Bellman partial differential equation (HJB-PDE) associated with a value function for the underlying process. Guided by this equation, we use a neural network to learn the zero variance change of measure. To improve performance of our estimation, we pursue two new ideas. First, we adopt a loss function that combines three objectives which collectively contribute to improving the performance of our estimator. Second, we embed our rare event problem into a sequence of problems with increasing rarity. We find that a well chosen schedule of rarity increase substantially speeds up rare event simulation. Our approach is illustrated on Brownian motion, Orstein-Uhlenbeck (OU) process, Cox–Ingersoll–Ross (CIR) process as well as Langevin double-well diffusion.

Författare

Henrik Hult

Kungliga Tekniska Högskolan (KTH)

Aastha Jain

Ashoka University

Sandeep Juneja

Ashoka University

Pierre Nyquist

Chalmers, Matematiska vetenskaper, Tillämpad matematik och statistik

Göteborgs universitet

Sushant Vijayan

Tata Institute of Fundamental Research

Proceedings - Winter Simulation Conference

08917736 (ISSN)

2559-2570
9798331534202 (ISBN)

2024 Winter Simulation Conference, WSC 2024
Orlando, USA,

Ämneskategorier (SSIF 2025)

Sannolikhetsteori och statistik

DOI

10.1109/WSC63780.2024.10838791

Mer information

Senast uppdaterat

2025-03-04