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Title: DifferentiableCosmologicalSimulationwithAdjointMethod Speaker:YinLi(李寅) PengchengLaboratory鹏城实验室 Abstract: Rapidadvancesindeeplearninghavebroughtnotonlymyriadpowerful neuralnetworks,butalsobreakthroughsthatbenefitestablished scientificresearch. Inparticular,automaticdifferentiation(AD)toolsandcomputational acceleratorslikeGPUshavefacilitatedforwardmodelingoftheUniverse withdifferentiablesimulations. Currentdifferentiablecosmologicalsimulationsarelimitedbymemory, thusaresubjecttoatrade-offbetweentimeandspace/massresolution. Theytypicallyintegrateforonlytensoftimesteps,unlikethe standardnon-differentiablesimulations. Wepresentanewapproachfreeofsuchconstraints,usingtheadjoint methodandreversetimeintegration. Itenableslargerandmoreaccurateforwardmodeling,andwillimprove gradientbasedoptimizationandinference. Weimplementitinaparticle-mesh(PM)N-bodylibrarypmwd (particle-meshwithderivatives). BasedonthepowerfulADsystemJAX,pmwdisfully differentiable,andishighlyperformantonGPUs. Bio: YinLiisaresearchscientistatthePengChengLaboratoryinShenzhen, China,workingattheinterfacebetweenmachinelearning,statistics, cosmologyandastrophysics. Previously,hewasaFlatironResearchFellowwithajointappointment attheCenterforComputationalAstrophysics(CCA)andtheCenterfor ComputationalMathematics(CCM)intheSimonsFoundation. Beforethat,hewasajointpostdoctoralcosmologistattheBerkeley CenterforCosmologicalPhysics(BCCP),UniversityofCalifornia, Berkeley,andtheKavliInstituteforthePhysicsandMathematicsofthe Universe(IPMU),UniversityofTokyo. HedidhisPh.D.andB.S.inphysics,respectivelyattheUniversityof ChicagoandPekingUniversity. Time:14:00-15:00PM,1/Mar,Wednesday Venue:Room508(largeseminarroom),DepartmentofAstronomy
更新时间:2023-04-15