3 papers
math.NA2026
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…
quant-ph2025
Schwinger-Keldysh non-perturbative field theory of open quantum systems beyond the Markovian regime: Application to spin-boson and spin-chain-boson models
Felipe Reyes-Osorio, Federico Garcia-Gaitan, David J. Strachan +3
We develop a unified framework for open quantum systems composed of many mutually interacting quantum spins, or any isomorphic systems like qubits and qudits, surrounded by one or…
math.NA2024
Convergence rates for random feature neural network approximation in molecular dynamics
Xin Huang, Petr Plechac, Mattias Sandberg +1
Random feature neural network approximations of the potential in Hamiltonian systems yield approximations of molecular dynamics correlation observables that have the expected error…