3 papers
stat.ML2026
Identifying parameter couplings and uncertainties of mixed-noise stochastic systems via full-covariance Gaussian mixture network
Xiaolong Wang, Xiangwen Hao, Jing Feng +2
Parameter identification of stochastic dynamical systems driven by mixed noises is challenging due to intractable likelihood functions. We propose PENN-GMD, a parameter estimation…
physics.comp-ph2026
A deep learning framework for jointly solving transient Fokker-Planck equations with arbitrary parameters and initial distributions
Xiaolong Wang, Jing Feng, Qi Liu +3
Efficiently solving the Fokker-Planck equation (FPE) is central to analyzing complex parameterized stochastic systems. However, current numerical methods lack parallel computation…
physics.comp-ph2025
The pseudo-analytical density solution to parameterized Fokker-Planck equations via deep learning
Xiaolong Wang, Jing Feng, Gege Wang +2
Efficiently solving the Fokker-Planck equation (FPE) is crucial for understanding the probabilistic evolution of stochastic particles in dynamical systems, however, analytical solu…