Building Blocks for Computer Vision with Stochastic Partial Differential Equations

Learning Partial Differential Equations for Computer Vision ∗

Partial differential equations (PDEs) have been successful for solving many problems in computer vision. However, the existing PDEs are all crafted by people with skill, based on some limited and intuitive considerations. As a result, the designed PDEs may not be able to handle complex situations in real applications. Moreover, human intuition may not apply if the vision task is hard to describ...

global results on some nonlinear partial differential equations for direct and inverse problems

در این رساله به بررسی رفتار جواب های رده ای از معادلات دیفرانسیل با مشتقات جزیی در دامنه های کراندار می پردازیم . این معادلات به فرم نیم-خطی و غیر خطی برای مسایل مستقیم و معکوس مورد مطالعه قرار می گیرند . به ویژه، تاثیر شرایط مختلف فیزیکی را در مساله، نظیر وجود موانع و منابع، پراکندگی و چسبندگی در معادلات موج و گرما بررسی می کنیم و به دنبال شرایطی می گردیم که متضمن وجود سراسری یا عدم وجود سراسر...

Stochastic Partial Differential Equations

Algorithm Refinement for Stochastic Partial Differential Equations

We construct a hybrid particle/continuum algorithm for linear diffusion in the fluctuating hydrodynamic limit. The particles act as independent random walkers and the fluctuating diffusion equation is solved by a finite difference scheme. At the interface between the particle and continuum computations the coupling is by flux matching, and yields exact mass conservation. This approach is an ext...

Postprocessing for Stochastic Parabolic Partial Differential Equations

We investigate the strong approximation of stochastic parabolic partial differential equations with additive noise. We introduce post-processing in the context of a standard Galerkin approximation, although other spatial discretizations are possible. In time, we follow [20] and use an exponential integrator. We prove strong error estimates and discuss the best number of postprocessing terms to ...