1 citations · 1 across the 1 of their papers we have counts for
4 papers
Data-driven Feynman-Kac Discovery with Applications to Prediction and Data Generation
Qi Feng, Guang Lin, Purav Matlia +1
In this paper, we propose a novel data-driven framework for discovering probabilistic laws underlying the Feynman-Kac formula. Specifically, we introduce the first stochastic SINDy…
PO-CKAN:Physics Informed Deep Operator Kolmogorov Arnold Networks with Chunk Rational Structure
Junyi Wu, Guang Lin
We propose PO-CKAN, a physics-informed deep operator framework based on Chunkwise Rational Kolmogorov--Arnold Networks (KANs), for approximating the solution operators of partial d…
Noise estimation of SDE from a single data trajectory
Munawar Ali, Purba Das, Qi Feng +2
In this paper, we propose a data-driven framework for model discovery of stochastic differential equations (SDEs) from a single trajectory, without requiring the ergodicity or stat…
Physics Informed Constrained Learning of Dynamics from Static Data
Pengtao Dang, Tingbo Guo, Melissa Fishel +4
A physics-informed neural network (PINN) models the dynamics of a system by integrating the governing physical laws into the architecture of a neural network. By enforcing physical…