TY - JOUR
T1 - Donsker-Varadhan large deviations for path-distribution dependent SPDEs
AU - Ren, Panpan
AU - Wang, Feng-Yu
PY - 2021/7/1
Y1 - 2021/7/1
N2 - As an important tool characterizing the long time behavior of Markov processes, the Donsker-Varadhan LDP (large deviation principle) does not directly apply to distribution dependent SDEs/SPDEs since the solutions are not standard Markovian. We establish this type LDP for several different models of distribution dependent SDEs/SPDEs which may also with memories, by comparing the original equations with the corresponding distribution independent ones. As preparations, the existence, uniqueness and exponential convergence are also investigated for path-distribution dependent SPDEs which should be interesting by themselves.
AB - As an important tool characterizing the long time behavior of Markov processes, the Donsker-Varadhan LDP (large deviation principle) does not directly apply to distribution dependent SDEs/SPDEs since the solutions are not standard Markovian. We establish this type LDP for several different models of distribution dependent SDEs/SPDEs which may also with memories, by comparing the original equations with the corresponding distribution independent ones. As preparations, the existence, uniqueness and exponential convergence are also investigated for path-distribution dependent SPDEs which should be interesting by themselves.
KW - Donsker-Varadhan LDP
KW - Path-distribution dependent SDEs
KW - Warsserstein distance
UR - http://www.scopus.com/inward/record.url?scp=85099993774&partnerID=8YFLogxK
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-85099993774&origin=recordpage
U2 - 10.1016/j.jmaa.2021.125000
DO - 10.1016/j.jmaa.2021.125000
M3 - RGC 21 - Publication in refereed journal
SN - 0022-247X
VL - 499
JO - Journal of Mathematical Analysis and Applications
JF - Journal of Mathematical Analysis and Applications
IS - 1
M1 - 125000
ER -