Path integrals formulations leading to propagator evaluation for coupled linear physics in large geometric models

Reformulating linear physics using second kind Fredholm equations is very standard practice. One of the straightforward consequences that resulting integrals can be expanded (when Neumann expansion converges) and probabilized, leading to path statistics Monte Carlo estimations. An essential feature these algorithms they also allow estimate propagators for all types sources, including initial conditions. The practice a single run, one given set producing later used with any other sources fast simulations, typically as parts optimization, inversion, sensitivity analysis command control algorithms. present paper illustrates how this extended problems involving several interacting physics, provided their coupling only at boundary system or interfaces between sub-parts, may itself form equation. A full practical implementation described part Stardis code, example transfering heat via radiation, reaction-diffusion convection expected in multidisciplinary context urban climate modeling. Besides, we show recent advances computer graphics indicate made numerically extremely efficient when facing large CAD geometries: computing propagator becomes strictly independent geometry refinement, i.e. identical whatever number triangles tetraedra numerize surface volume descriptions. To best our knowledge first report computations remains coupled geometries. Program Title: 0.7.2 (built on stardis-solver 0.12.3) CPC Library link program files: https://doi.org/10.17632/k76zrx4n6b.1 Developer's repository link: https://www.meso-star.com/projects/stardis/stardis.html Licensing provisions: GPLv3 Programming language: ANSI C Python Nature problem: Estimating temperatures transfer systems and/or numbers spatially distributed sources. Solution method: uses Method. Each emperature constructs each energy within system: temperature, temperature conditions, powers fluxes. stored further use outside code. Additional comments restrictions unusual features: estimates are reliable radiative linearized around reference temperature. deal nonlinearity radiation temperatures, but then nothing interpreted meaningfull external usage. scripts were execute different configurations, post-process results generate graphs.