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SUMMARY:Neural functional theory for inhomogeneous (non-)equilibrium fluid
 s - Florian Sammüller\, University of Bayreuth
DTSTART:20240205T143000Z
DTEND:20240205T150000Z
UID:TALK211741@talks.cam.ac.uk
CONTACT:Dr Philipp Pracht
DESCRIPTION:Classical density functional theory and power functional theor
 y provide formally exact frameworks for the description of many-body syste
 ms in and out of equilibrium. In the talk I show how machine learning can 
 be applied effectively in these theories in order to characterize inhomoge
 neous fluids. Neural networks which are trained with simulation data facil
 itate precise and flexible representations of the central functional maps.
  These neural functionals can be utilized straightforwardly for prediction
 s which supersede analytic treatments in accuracy. Additionally\, they ena
 ble access to more fundamental related quantities\, which forms the basis 
 of a stand-alone theoretical framework. Successful applications include mu
 ltiscale problems\, such as colloidal sedimentation-diffusion equilibrium 
 under gravity\, and the inverse design of nonequilibrium flow.\n\n* F. Sam
 müller\, S. Hermann\, and M. Schmidt\, arXiv:2312.04681\n* F. Sammüller\
 , S. Hermann\, D. de las Heras\, and M. Schmidt\, Proc. Nat. Acad. Sci. 12
 0\, e2312484120 (2023)\, doi:10.1073/pnas.2312484120\n* D. de las Heras\, 
 T. Zimmermann\, F. Sammüller\, S. Hermann\, and M. Schmidt\,\nJ. Phys.: C
 ondens. Matter 35\, 271501 (2023)\, doi:10.1088/1361-648X/accb33
LOCATION:Zoom link: https://zoom.us/j/92447982065?pwd=RkhaYkM5VTZPZ3pYSHpt
 UXlRSkppQT09
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