\documentclass[11pt]{report} \usepackage{setspace} \doublespacing \usepackage[toc,page]{appendix} \usepackage[]{amsmath} \usepackage[]{amsthm} \usepackage{mathtools} \numberwithin{equation}{section} \usepackage[]{amssymb} \usepackage[margin=1in]{geometry} \usepackage[]{soul} \usepackage[]{bbm} \usepackage[]{cancel} \usepackage[]{xcolor} \usepackage[]{enumitem} \usepackage{mathrsfs} \usepackage{hyperref} \usepackage[capitalise]{cleveref} \usepackage{natbib} \usepackage{neuralnetwork} \usepackage{witharrows} \usepackage{listings} \usepackage{graphicx} \DeclareMathAlphabet{\mymathbb}{U}{BOONDOX-ds}{m}{n} % \usepackage[]{enumerate} \setlength\parindent{0pt} \DeclareMathOperator{\Trace}{Trace} \DeclareMathOperator{\Hess}{Hess} \DeclareMathOperator{\supp}{supp} \DeclareMathOperator{\domain}{Domain} \DeclareMathOperator{\lip}{Lip} \DeclareMathOperator{\diag}{diag} \usepackage{tikz-cd} \DeclareMathOperator{\rect}{Rect} \DeclareMathOperator{\param}{Param} \DeclareMathOperator{\inn}{In} \DeclareMathOperator{\out}{Out} \DeclareMathOperator{\neu}{NN} \DeclareMathOperator{\hid}{Hid} \DeclareMathOperator{\lay}{Lay} \DeclareMathOperator{\dep}{Dep} \DeclareMathOperator{\we}{Weight} \DeclareMathOperator{\bi}{Bias} \DeclareMathOperator{\aff}{Aff} \DeclareMathOperator{\act}{Act} \DeclareMathOperator{\real}{Rlz} \DeclareMathOperator{\id}{Id} \DeclareMathOperator{\mult}{Mult} \DeclareMathOperator{\wid}{Wid} \DeclareMathOperator{\sm}{Sum} \DeclareMathOperator{\trn}{Trn} \DeclareMathOperator{\cpy}{Copy} \DeclareMathOperator{\ex}{Ex} \DeclareMathOperator{\lin}{Lin} \DeclareMathOperator{\relu}{ReLU} \DeclareMathOperator{\zero}{Zr} \DeclareMathOperator{\obj}{obj} \DeclareMathOperator{\dom}{dom} \DeclareMathOperator{\cod}{cod} \newcommand{\bbP}{\mathbb{P}} \newcommand{\E}{\mathbb{E}} \newcommand{\R}{\mathbb{R}} \newcommand{\Z}{\mathbb{Z}} \newcommand{\N}{\mathbb{N}} \newcommand{\p}{\mathfrak{p}} \newcommand{\mft}{\mathfrak{t}} \newcommand{\f}{\mathfrak{f}} \newcommand{\C}{\mathfrak{C}} \newcommand{\n}{\mathscr{N}} \newcommand{\lp}{\left(} \newcommand{\rp}{\right)} \newcommand{\rb}{\right]} \newcommand{\lb}{\left[} \newcommand{\lv}{\left|} \newcommand{\rv}{\right|} \newcommand{\la}{\langle} \newcommand{\ra}{\rangle} \newcommand{\ve}{\varepsilon} \newcommand{\les}{\leqslant} \newcommand{\ges}{\geqslant} % Input layer neurons'number \newcommand{\inputnum}{3} % Hidden layer neurons'number \newcommand{\hiddennum}{5} % Output layer neurons'number \newcommand{\outputnum}{2} \newtheorem{theorem}{Theorem}[section] \newtheorem{corollary}{Corollary}[theorem] \newtheorem{lemma}[theorem]{Lemma} \newtheorem{definition}[theorem]{Definition} \newtheorem{remark}[theorem]{Remark} \newtheorem{claim}[theorem]{Claim} \title{Reformulation without f} \author{Shakil Rafi} \begin{document} \maketitle \begin{lemma} Let $T \in (0,\infty)$, let $\lp \Omega, \mathcal{F}, \mathbb{P}\rp $ be a probability space, let $\alpha_d \in \R^d \rightarrow \R$, $d \in \N$, be infinitely often differentiable functions, let $u_d \in C^{1,2} \lp \lb 0,T \rb \times, \R^d, \R \rp$, $d \in \N$, satisfy for all $d \in \N$, $t \in \lb 0,T\rb$, $x \in \R^d$ that: \begin{align} \lp \frac{\partial}{\partial t} u_d \rp \lp t,x \rp + \lp \Delta_x u_d \rp \lp t,x \rp + \alpha_x \lp x \rp u_d \lp t,x \rp = 0 \end{align} Let $\mathcal{W}^d: \lb 0,T \rb \times \Omega \rightarrow \R^d$, $d \in \N$ be standard Brownian motions, and let $\mathcal{X}^{d,t,x}: \lb t,T\rb \times \Omega \rightarrow \R^d$, $d \in \N$, $t\in \lb 0,T \rb$, $s \in \lb t,T\rb$, $x\in \R^d$ we have $\mathbb{P}$-a.s. that: \begin{align} \mathcal{X}^{d,t,x}_s = x+ \int^t_s \sqrt{2} d\mathcal{W}^d_r \end{align} Then for all $d \in \N$, $t\in \lb 0,T\rb$, $x\in \R^d$ it holds that: \begin{align} u_d\lp t,x \rp + \mathbb{E} \lb \exp \lp \int^T_t \alpha_x \lp \mathcal{X}^{d,t,x}_r \rp dr \rp u_d \lp T, \mathcal{X}^{d,t,x}_T \rp \rb \end{align} \end{lemma} \begin{proof} Let $T \in [0,\infty)$, and let $\lp \Omega, \mathcal{F}, \mathbb{P} \rp$ be a probability space. For all $d \in \N$, let $V \in C^{1,1} \lp \R^d \times [0,T],\R \rp $ be $V(x,t) = \alpha_d(x)$, let $\sigma_d : \R^d \rightarrow \R^{d\times d}$ be given by $\sigma_d(x) = \diag_d \lp \sqrt{2} \rp$, let $\mu_d: \R^d \rightarrow \R^d$ be given by $\mu_d(x) = \mymathbb{0}_d$, and finally let $f(t,x) = 0$. By Feynman-Kac and substituting the above, the following expression: \begin{align}\label{0.0.4} \lp \frac{\partial}{\partial t} u_d \rp \lp t,x \rp + \frac{1}{2}\Trace\lp \sigma(t,x) \lb \sigma(t,x) \rb^* \lp \Hess_x(u_d \rp \lp t,x \rp \rp + \la \mu(t,x), \lp \nabla_x u_d \rp \lp t,x\rp \ra +V(t,x)u_d(t,x) \nonumber\\ +f(t,x) = 0 \end{align} is rendered: \begin{align}\label{0.0.5} \lp \frac{\partial}{\partial t} u_d \rp \lp t,x \rp + \lp \Delta_x u_d \rp \lp t,x\rp + \alpha_d(x)u_d(x) = 0 \end{align} Note then that Feyman-Kac sates that the solution to (\ref{0.0.4}) can be written as: \begin{align} u(t,x) = \E \lb \int^T_t e^{\int^r_t V (\mathcal{X}_t,\tau) d\tau}f(\mathcal{X}_r, r )dr + e^{-\int^T_t V(\mathcal{X}_\tau,\tau)d\tau } u(\mathcal{X}_T,T) \rb \end{align} Where $\mathcal{X}$ is an $\lp \Omega, \mathcal{F}, \mathbb{P} \rp$-adapted stochastic process given by: \begin{align} \mathcal{X}_t = x + \int^t_s \mu_d \lp \mathcal{X} \rp dr+\int^t_s\sqrt{2}d\mathcal{W}_r^d \end{align} Note then that the substitutions then yield that the solution to (\ref{0.0.5}) is given by: \begin{align} u(t,x) = \E \lb \exp \lp \int^T_t \alpha_d \lp \mathcal{X} \rp dr \rp u_d \lp T,\mathcal{X}^{d,t,x}_T \rp \rb \end{align} \end{proof} \end{document}