9 citations · 20 across the 8 of their papers we have counts for
10 papers
Generative diffusion learning for parametric partial differential equations
Ting Wang, Petr Plechac, Jaroslaw Knap
We develop a class of data-driven generative models that approximate the solution operator for parameter-dependent partial differential equations (PDE). We propose a novel probabil…
Martingale product estimators for sensitivity analysis in computational statistical physics
Petr Plechac, Gabriel Stoltz, Ting Wang
We introduce a new class of estimators for the linear response of steady states of stochastic dynamics. We generalize the likelihood ratio approach and formulate the linear respons…
Stochastic Gradient Descent for Semilinear Elliptic Equations with Uncertainties
Ting Wang, Jaroslaw Knap
Randomness is ubiquitous in modern engineering. The uncertainty is often modeled as random coefficients in the differential equations that describe the underlying physics. In this…
Convergence of the likelihood ratio method for linear response of non-equilibrium stationary states
Petr Plechac, Gabriel Stoltz, Ting Wang
We consider numerical schemes for computing the linear response of steady-state averages of stochastic dynamics with respect to a perturbation of the drift part of the stochastic d…
Accelerated scale bridging with sparsely approximated Gaussian learning
Ting Wang, Kenneth W. Leiter, Petr Plechac +1
Multiscale modeling is a systematic approach to describe the behavior of complex systems by coupling models from different scales. The approach has been demonstrated to be very eff…
On the Validity of the Girsanov Transformation Method for Sensitivity Analysis of Stochastic Chemical Reaction Networks
Ting Wang, Muruhan Rathinam
We investigate the validity of the Girsanov Transformation (GT) method for parametric sensitivity analysis of stochastic models of chemical reaction networks. The validity depends…
Steady State Sensitivity Analysis of Continuous Time Markov Chains
Ting Wang, Petr Plechac
In this paper we study Monte Carlo estimators based on the likelihood ratio approach for steady-state sensitivity. We first extend the result of Glynn and Olvera-Cravioto [doi:doi:…
Parallel replica dynamics method for bistable stochastic reaction networks: simulation and sensitivity analysis
Ting Wang, Petr Plecháč
Stochastic reaction networks that exhibit bistability are common in many fields such as systems biology and materials science. Sampling of the stationary distribution is crucial fo…
Stationary averaging for multi-scale continuous time Markov chains using parallel replica dynamics
Ting Wang, Petr Plecháč, David Aristoff
We propose two algorithms for simulating continuous time Markov chains in the presence of metastability. We show that the algorithms correctly estimate, under the ergodicity assump…
Efficiency of the Girsanov transformation approach for parametric sensitivity analysis of stochastic chemical kinetics
Ting Wang, Muruhan Rathinam
Most common Monte Carlo methods for sensitivity analysis of stochastic reaction networks are the finite difference (FD), the Girsanov transformation (GT) and the regularized pathwi…