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+\documentclass[9pt]{beamer}
+\renewcommand{\emph}[1]{\textcolor{blue}{#1}}
+\newif\iflong
+\longfalse
+\setbeamerfont{footnote}{size=\scriptsize}
+
+\input{header}
+\input{macros}
+
+\newcommand{\highlight}[2]{%
+  \colorbox{#1!20}{$\displaystyle#2$}}
+
+\newcommand{\hiat}[4]{%
+  \only<#1>{\highlight{#3}{#4}}%
+  \only<#2>{\highlight{white}{#4}}%
+}
+
+\graphicspath{{figures/}}
+\AtEveryCitekey{\clearfield{pages}}
+\AtEveryCitekey{\clearfield{eprint}}
+\AtEveryCitekey{\clearfield{volume}}
+\AtEveryCitekey{\clearfield{number}}
+\AtEveryCitekey{\clearfield{month}}
+\addbibresource{main.bib}
+
+\title{Mobility estimation for Langevin dynamics using control variates\\[.3cm]
+  \small \textcolor{yellow}{AMMP Seminar}
+}
+
+\author{%
+    Urbain Vaes \texorpdfstring{\\\texttt{urbain.vaes@inria.fr}}{}
+}
+
+\institute{%
+    MATHERIALS -- Inria Paris
+    \textcolor{blue}{\&} CERMICS --
+    École des Ponts ParisTech
+}
+
+\date{October 2022}
+\begin{document}
+
+
+\begin{frame}[plain]
+  \begin{figure}[ht]
+    \centering
+    % \includegraphics[height=1.5cm]{figures/logo_matherials.png}
+    % \hspace{.5cm}
+    \includegraphics[height=1.2cm]{figures/logo_inria.png}
+    \hspace{.5cm}
+    \includegraphics[height=1.5cm]{figures/logo_ponts.png}
+    \hspace{.5cm}
+    \includegraphics[height=1.5cm]{figures/logo_ERC.jpg}
+    \hspace{.5cm}
+    \includegraphics[height=1.5cm]{figures/logo_EMC2.png}
+  \end{figure}
+  \titlepage
+\end{frame}
+
+\begin{frame}
+  {Outline}
+  \tableofcontents
+\end{frame}
+
+% \section{Some background material on fast/slow systems of SDEs}%
+% \label{sec:numerical_solution_of_multiscale_sdes}
+
+
+% \begin{frame}
+%   {Homogenization result}
+%   \begin{itemize}
+%     \item Effective drift:
+%     \[
+%       \vect F(x) = \int_{\torus^n} \left(\vect f \, \cdot \, \grad_x \right) \vect \Phi(x,y) \, \rho^{\infty}(y;x) \, \d y.
+%     \]
+%     \item Effective diffusion:
+%       \begin{align*}
+%         & \mat A(x) \, \mat A(x)^T = \frac12 \left(\mat A_0(x) + \mat A_0(x)^T\right), \\
+%         & \text{with } \mat A_0(x) :=  2 \int_{\real^n} \vect f(x,y) \, \otimes \, \vect \Phi(x,y) \, \rho^{\infty}(y;x) \, \d y.
+%       \end{align*}
+%   \end{itemize}
+%   \begin{example}
+%     Multiscale system:
+%     \begin{alignat*}{2}
+%       & \d X^{\varepsilon}_t = \frac{1}{\varepsilon} X^{\varepsilon}_t \, Y^{\varepsilon}_t \, \d t, \quad & X^{\varepsilon}_0 = 1, \\
+%       & \d Y^{\varepsilon}_t = - \frac{1}{\varepsilon^2} \, Y_t^{\varepsilon} \, \d t
+%         + \frac{\sqrt 2}{\varepsilon}  \,\d W_{y}(t), \quad & Y^{\varepsilon}_0 = 0.
+%     \end{alignat*}
+%     Effective equation:
+%     \[
+%       \d X_t = X_t \, \d t + \, \sqrt{2} \, X_t \, \d W_{y} (t).
+%     \]
+%   \end{example}
+% \end{frame}
+
+% \begin{frame}
+%   {Example: Stratonovich correction}
+%   \begin{figure}[ht]
+%   \centering
+%   \href{run:videos/spectral/slow.avi?autostart&loop}%
+%   {\includegraphics[width=0.8\textwidth]{videos/spectral/slow.png}}%
+
+%   \href{run:videos/spectral/fast.avi?autostart&loop}%
+%   {\includegraphics[width=0.8\textwidth]{videos/spectral/fast.png}}%
+%   \caption{%
+%     Convergence to the solution of the effective equation as $\varepsilon \to 0$.
+%   }
+%   \end{figure}
+% \end{frame}
+
+\section{Mobility estimation for Langevin dynamics using control variates}
+\begin{frame}
+    {Collaborators and reference}
+    \begin{figure}
+        \centering
+        \begin{minipage}[t]{.2\linewidth}
+            \centering
+            \raisebox{\dimexpr-\height+\ht\strutbox}{%
+              \includegraphics[height=\linewidth]{figures/collaborators/greg.jpg}
+            }
+        \end{minipage}\hspace{.01\linewidth}%
+        \begin{minipage}[t]{.24\linewidth}
+            Grigorios Pavliotis
+            \vspace{0.2cm}
+
+            \includegraphics[height=1cm,width=\linewidth,keepaspectratio]{figures/collaborators/imperial.pdf}
+            \flushleft \scriptsize
+            Department of Mathematics
+        \end{minipage}\hspace{.1\linewidth}%%
+        \begin{minipage}[t]{.2\linewidth}
+            \centering
+            \raisebox{\dimexpr-\height+\ht\strutbox}{%
+              \includegraphics[height=\linewidth]{figures/collaborators/gabriel.jpg}
+            }
+        \end{minipage}\hspace{.01\linewidth}%
+        \begin{minipage}[t]{.24\linewidth}
+          Gabriel Stoltz
+          \vspace{0.2cm}
+
+          \includegraphics[height=1.2cm,width=\linewidth,keepaspectratio]{figures/collaborators/enpc.png}
+          \flushleft \scriptsize
+          CERMICS
+        \end{minipage}
+    \end{figure}
+
+    \vspace{.7cm}
+    \textbf{Reference:}
+     \fullcite{2022arXiv220609781P}
+\end{frame}
+
+
+% \begin{frame}[plain]
+%   \frametitle{Outline}
+%   \tableofcontents[subsectionstyle=show]
+% \end{frame}
+
+\subsection{Background and problem statement}%
+
+\AtBeginSubsection[]
+{
+  \begin{frame}<beamer>
+    % \frametitle{Outline for section \thesection}
+    \frametitle{Outline}
+    \tableofcontents[currentsubsection,sectionstyle=show/shaded,subsectionstyle=show/shaded/hide]
+    % \tableofcontents[currentsubsection]
+  \end{frame}
+}
+
+\begin{frame}
+  {Goals of molecular dynamics}
+
+  {\large $\bullet$} Computation of \emph{macroscopic properties} from Newtonians atomistic models:
+  \vspace{-.1cm}
+  \begin{minipage}{.51\textwidth}
+    \vspace{-.7cm}
+    \begin{itemize}
+      \item Static properties, such as
+        \begin{itemize}
+          \item the heat capacity and
+          \item the equations of state $P = P(\rho, T)$.
+        \end{itemize}
+
+        \vspace{.2cm}
+      \item Dynamical properties, such as \emph{transport coefficients}:
+        % mobilité,
+        % viscosité de cisaillement;
+        % conductivité thermique.
+        \begin{itemize}
+          \item the viscosity;
+          \item the thermal conductivity;
+          \item the \emph{mobility} of ions in solution.
+        \end{itemize}
+    \end{itemize}
+  \end{minipage}
+  \hspace{.5cm}
+  \begin{minipage}{.4\textwidth}
+    \begin{figure}[ht]
+      \centering
+      \includegraphics[width=.8\linewidth, angle=270]{figures/loi_argon-crop.pdf}
+      \caption*{\hspace{1.2cm}%
+        Equation of state of argon at 300K.
+
+        \tiny\hspace{1.2cm}$\bullet$ `+': molecular simulation;
+
+        \hspace{1.2cm}$\bullet$ Solid line: experimental measurements\footnotemark.
+      }
+    \end{figure}
+  \end{minipage}
+  \footnotetext{\url{https://webbook.nist.gov/chemistry/fluid/}}
+
+  \vspace{.2cm}
+  {\large $\bullet$} \emph{Numerical microscope}:
+  used in physics, biology, chemistry.
+\end{frame}
+
+\begin{frame}
+  {Some background material on the Langevin equation}
+  Consider the (one-particle) Langevin equation
+  \[
+    \left\{
+    \begin{aligned}
+      & \d \vect q_t = \textcolor{blue}{\vect p_t \, \d t}, \\
+      & \d \vect p_t = \textcolor{blue}{- \grad V(\vect q_t) \, \d t} \, \textcolor{red}{- {\color{black}\gamma} \vect p_t \, \d t + \sqrt{2 {\color{black}\gamma} \beta^{-1}} \, \d \vect W_t},
+    \end{aligned}
+    \right.
+    \qquad (\vect q_0, \vect p_0) \sim \mu,
+  \]
+  where $\gamma$ is the friction, $V$ is a \emph{periodic} potential, and $\beta = \frac{1}{k_{\rm B} T}$.
+  \begin{itemize}
+    % \item The dynamics is composed of a \textcolor{blue}{Hamiltonian} part and a \textcolor{red}{fluctuation/dissipation} part;
+    \item The invariant probability measure is
+      \[
+        \mu(\vect q, \vect p) = \frac{1}{Z} \e^{-\beta H(\vect q, \vect p)} = \frac{1}{Z} \e^{-\beta \left(V(\vect q) + \frac{\abs{\vect p}^2}{2}\right)}, \quad \text{on}~ \emph{\torus^d} \times \real^d.
+      \]
+    \item The generator of the associated Markov semigroup
+      \[
+        \left (\e^{\mathcal L t} \varphi\right) (\vect q, \vect p) = \expect \bigl(\varphi(\vect q_t, \vect p_t) \big| (\vect q_0, \vect p_0) = (\vect q, \vect p) \bigr)
+      \]
+      is the following operator:
+      \begin{align*}
+        \mathcal L &= \textcolor{blue}{\left(\vect p \cdot \grad_{\vect q} - \grad V(q) \cdot \grad_{\vect p} \right)}
+        + \gamma \, \textcolor{red}{\left( - \vect p \grad_{\vect p} + \beta^{-1} \laplacian_{\vect p} \right)}
+        =: \textcolor{blue}{\mathcal L_{\textrm{ham}}} + \gamma \, \textcolor{red}{\mathcal L_{\textrm{FD}}}.
+      \end{align*}
+  \end{itemize}
+  We denote by $\norm{\cdot}$ and $\ip{\cdot}{\cdot}$ the norm and inner product of~$L^2(\mu)$, and
+  \[
+    L^2_0(\mu) = \Bigl\{\varphi \in L^2(\mu) : \ip{\varphi}{1} = \expect_{\mu} \varphi =  0 \Bigr\}.
+  \]
+\end{frame}
+
+
+% \begin{frame}
+%   {Common models in molecular simulation}
+%   We consider the following hierarchy of models:
+%   \begin{align}
+%     \label{eq:gle:model:overdamped} \tag{OL}
+%     \dot {\vect q} &= - \grad V(\vect q) + \sqrt{2 \, \beta^{-1}} \, \dot {\vect W}, \\
+%     \label{eq:gle:model:langevin} \tag{L}
+%     \ddot {\vect q} &= - \grad V(\vect q) - \gamma \, \dot {\vect q} + \sqrt{2 \gamma \, \beta^{-1}} \, \dot {\vect W}, \\
+%     \label{eq:gle:model:generalized} \tag{GLE}
+%     \ddot {\vect q} &= -\grad V(\vect q) - \int_{0}^{t} \widehat \gamma(t-s) \, \dot {\vect q}(s) \, \d s + \vect F(t).
+%   \end{align}
+%   where
+%   \begin{itemize}
+%     \item $V$ is a potential, in this talk \emph{periodic};
+%     \item $\gamma$ is the friction coefficient;
+%     \item $\widehat \gamma(\cdot)$ is the memory kernel;
+%     \item $\vect F$ is a stationary non-Markovian noise process.
+%   \end{itemize}
+%   \vspace{.2cm}
+
+%   The kernel $\widehat \gamma(\cdot)$ and the noise $F$ are related by the \emph{fluctuation/dissipation} relation:
+%   \[
+%       \expect\bigl[\vect F(t) \otimes \vect F(s)\bigr] = \beta^{-1} \, \widehat \gamma(t-s) \mat I_d.
+%   \]
+% \end{frame}
+
+% \subsection{Mobility and effective diffusion}
+\begin{frame}
+  {Definition of the mobility}
+  Consider Langevin dynamics with additional forcing in a direction $\vect e$:
+  % \[
+  %   \ddot {\vect q} = - \grad V(\vect q) + \alert{\eta \vect e} - \gamma \, \dot {\vect q} + \sqrt{2 \, \gamma} \, \beta^{-1} \, \dot {\vect W}.
+  % \]
+  % This equation may be rewritten as a system for the position and momentum:
+  \[
+    \left\{
+    \begin{aligned}
+      & \d \vect q_t = \vect p_t \, \d t, \\
+      & \d \vect p_t = - \grad V(\vect q_t) \, \d t + \alert{\eta \vect e} \, \d t - \gamma \vect p_t \, \d t + \sqrt{2 \gamma \beta^{-1}} \, \d \vect W_t.
+    \end{aligned}
+    \right.
+  \]
+  This dynamics admits a unique invariant probability distribution $\mu_{\alert{\eta}} \in \mathcal P(\emph{\torus^d} \times \real^d)$.
+
+  \begin{definition}
+    [Mobility]
+    The mobility in direction $\vect e$ is defined mathematically as
+    \[
+      M_{\vect e} =
+      \lim_{\alert{\eta} \to 0} \frac{1}{\alert{\eta}}\expect_{\mu_{\alert{\eta}}} [\vect e^\t \vect p]
+    \]
+    $\approx $ factor relating the mean momentum to the strength of the inducing force.
+  \end{definition}
+
+  \begin{itemize}
+    \item There is a symmetric mobility tensor $\mat M$ such that $M_{\vect e} = \vect e^\t \mat M \vect e$.
+
+    \item
+      \textbf{Einstein's relation:}
+      \(
+      \mat D = \beta^{-1} \mat M,
+      \) with $\mat D$ the \emph{effective diffusion coefficient}.
+  \end{itemize}
+\end{frame}
+
+\begin{frame}
+  {Effective diffusion}
+  It is possible to show a \emph{functional central limit theorem} for the Langevin dynamics\footfullcite{MR663900}:
+  \begin{equation*}
+    \varepsilon  \vect q_{s/\varepsilon^2} \xrightarrow[\varepsilon \to 0]{} \sqrt{2 \mat D} \, \vect W_s
+    \qquad  \text{weakly on } C([0, \infty)).
+  \end{equation*}
+  In particular, $\vect q_t/\sqrt{t} \xrightarrow[t \to \infty]{} \mathcal N(0, 2 \mat D)$ weakly.
+
+  \vspace{-.25cm}
+  \begin{figure}[ht]
+  \centering
+  \href{run:videos/gle/effective-diffusion.webm?autostart&loop}%
+  {\includegraphics[width=0.75\textwidth]{videos/gle/effective-diffusion.png}}%
+  \caption{Histogram of $q_t/\sqrt{t}$. The potential $V(q) = - \cos(q) / 2$ is illustrated in the background.}
+  \end{figure}
+\end{frame}
+
+\begin{frame}
+  {Mathematical expression for the effective diffusion (dimension 1)}
+  \vspace{.2cm}
+  \begin{exampleblock}{Expression of $D$ in terms of the solution to a Poisson equation}
+  The effective diffusion coefficient is given by where $D = \emph{ \ip{\phi}{p}}$ and $\phi$ is the solution to
+  \[
+    \emph{- \mathcal L \phi = p},
+    \qquad \phi \in L^2_0(\mu) := \bigl\{ u \in L^2(\mu): \ip{u}{1} = 0 \bigr\}.
+  \]
+  \end{exampleblock}
+  \textbf{Key idea of the proof:} Apply It\^o's formula to $\phi$
+  \begin{align*}
+    \d \phi(q_s, p_s)
+    % &= \frac{1}{\varepsilon^2} \mathcal L_{L} \phi (q_t, p_t)  + \frac{1}{\varepsilon} \, \sqrt{2 \gamma \beta^{-1}} \, \derivative{1}[\phi]{p}(q_t, p_t) \, \d W_t, \\
+    &= - p_s \, \d s + \sqrt{2 \gamma \beta^{-1}} \, \derivative{1}[\phi]{p}(q_s, p_s) \, \d W_s
+  \end{align*}
+  and then rearrange:
+  \begin{align*}
+    \alert\varepsilon (q_{t/\alert\varepsilon^2} - q_{0}) &= \alert\varepsilon \int_{0}^{t/\alert\varepsilon^2} p_s \, \d s \\
+                                                            &= \underbrace{\alert\varepsilon \bigl(\phi(q_0, p_0) - \phi(q_{t/\alert\varepsilon^2}, p_{t/\alert\varepsilon^2})\bigr)}_{\to 0
+                                                            % ~\text{in $L^p(\Omega, C([0, T], \real))$}
+                                                          }
+    + \underbrace{\sqrt{2 \gamma \beta^{-1}} \alert\varepsilon \int_{0}^{t/\alert\varepsilon^2} \derivative{1}[\phi]{p}(q_s, p_s) \, \d W_s}_{\to \sqrt{2 D} W_t~\text{weakly by MCLT}}.
+  \end{align*}
+  % where
+  % \begin{align*}
+  %   D &= \gamma \beta^{-1} \, \int \abs{\textstyle \derivative{1}[\phi]{p}(q, p)}^2 \, \mu(\d q \, \d p)
+  %   = - \int \phi (\mathcal L \phi) \, \d \mu
+  %   = \ip{\phi}{p}.
+  % \end{align*}
+
+  \vspace{.3cm}
+  \textbf{In the multidimensional setting}, $D_{\vect e} = \ip{\phi_{\vect e}}{\vect e^\t \vect p}$ with $- \mathcal L \phi_{\vect e} = \vect e^\t \vect p$
+\end{frame}
+
+\begin{frame}
+  {Open question: surface diffusion when $\gamma \ll 1$\footnote{Source of the video: \url{https://en.wikipedia.org/wiki/Surface_diffusion}}}
+    \begin{figure}[ht]
+        \centering
+        \href{run:videos/surface_diffusion.webm?autostart&loop}%
+        {\includegraphics[width=0.4\linewidth]{videos/surface_diffusion.png}}
+        \hspace{1cm}
+        % \href{run:videos/diffusion.webm?autostart&loop}%
+        % {\includegraphics[width=0.4\linewidth]{figures/mean_square.pdf}}
+    \end{figure}
+
+    \vspace{-.3cm}
+    Applications:
+    \begin{itemize}
+      \item integrated circuits;
+      \item catalysis.
+    \end{itemize}
+
+    \textbf{Open question}: behavior of the effective diffusion coefficient when $\gamma \ll 1$?
+    \[
+      D = \lim_{t \to \infty} \frac{\langle \abs{\vect q(t)}^2 \rangle}{4 t} \sim \gamma^{-\alert{\sigma}}, \qquad \alert{\sigma} =\, ???
+    \]
+    % \vspace{-.3cm}
+
+    % \textbf{Difficulty}: slow convergence of Monte Carlo methods when $\gamma$ is small.
+    % \vspace{.3cm}
+\end{frame}
+
+
+% \subsection{Some background material on the Langevin equation}
+
+
+\begin{frame}{Langevin dynamics: \textcolor{yellow}{underdamped} and \textcolor{yellow}{overdamped} regimes\footfullcite{MR2394704}}
+  \begin{figure}[ht]
+    \centering
+    \href{run:videos/particles_underdamped.webm?autostart&loop}%
+    {\includegraphics[width=0.49\textwidth]{videos/particles_underdamped.png}}%
+    \href{run:videos/particles_overdamped.webm?autostart&loop}%
+    {\includegraphics[width=0.49\textwidth]{videos/particles_overdamped.png}}%
+    \caption{Langevin dynamics with friction $\gamma = 0.1$ (left) and $\gamma = 10$ (right)}
+  \end{figure}
+
+  \vspace{-.3cm}
+  \begin{itemize}
+    \item The \alert{underdamped} limit as $\gamma \to 0$ is well understood in dimension 1 but not in the \alert{multi-dimensional setting}.
+    \item \emph{Overdamped} limit:
+      as $\gamma \to \infty$, the rescaled process $t \mapsto q_{\gamma t}$ converges weakly to the solution of the \emph{overdamped Langevin equation}:
+      \[
+        \dot {\vect q} = - \grad V(q) + \sqrt{2 \, \beta^{-1}} \, \dot {\vect W}.
+      \]
+  \end{itemize}
+\end{frame}
+
+\begin{frame}
+  {The \textcolor{yellow}{underdamped} limit in \textcolor{yellow}{dimension 1}}
+  As \emph{$\gamma \to 0$},
+  the Hamiltonian of the rescaled process
+  \begin{equation*}
+    \left\{
+    \begin{aligned}
+    q_{\gamma}(t) = q(t/\gamma), \\
+    p_{\gamma}(t) = p(t/\gamma),
+    \end{aligned}
+    \right.
+  \end{equation*}
+  converges weakly to a diffusion process on a graph.
+  \vspace{-.6cm}
+
+  \begin{figure}[ht!]
+      % \centering
+      % #1f77b4', u'#ff7f0e', u'#2ca02c
+      \definecolor{c1}{RGB}{31,119,180}
+      \definecolor{c2}{RGB}{255,127,14}
+      \definecolor{c3}{RGB}{44,160,44}
+      \begin{tikzpicture}%
+      \node[anchor=south west,inner sep=0] at (0,0) {%
+          \includegraphics[width=.7\textwidth]{figures/separatrix.eps}
+      };
+      \coordinate (origin) at (10,0);
+      \coordinate (Emin) at ($ (origin) + (0,.5) $);
+      \coordinate (E0) at ($ (origin) + (0,2) $);
+      \coordinate (E1) at ($ (origin) + (-1,4) $);
+      \coordinate (E2) at ($ (origin) + (1,4) $);
+      \node at ($ (Emin) + (.7,0) $) {$E_{\min}$};
+      \node[color=red] at ($ (E0) + (.5,0) $) {$E_{0}$};
+      \node at ($ (E1) + (0,.3) $) {$p < 0$};
+      \node at ($ (E2) + (0,.3) $) {$p > 0$};
+      \draw[thick,color=c2] (Emin) -- (E0) node [color=black, midway, right] {};
+      \draw[thick,color=c1] (E0) -- (E1) node [color=black, midway, left] {};
+      \draw[thick,color=c3] (E0) -- (E2) node [color=black, midway, right] {};
+      \node at (E0) [circle,fill,inner sep=1.5pt,color=red]{};
+      \node at (Emin) [circle,fill,inner sep=1.5pt]{};
+      \end{tikzpicture}%
+  \end{figure}
+  \vspace{-.5cm}
+  In this limit, it holds that
+  \[
+    % \norm{\mathcal L^{-1}}_{\mathcal B\left(L^2_0(\mu)\right)} = \mathcal O \left( \alert{\gamma^{-1}} \right),
+    % \qquad
+    \phi = - \mathcal L^{-1} p = \alert{\gamma^{-1}} \phi_{\rm und} + \mathcal O(\gamma^{-1/2}).
+  \]
+  % The limiting function $\phi_{\rm und}$ is continuous but \alert{not in $H^1(\mu)$}.
+\end{frame}
+
+
+\begin{frame}
+  {Scaling of the effective diffusion coefficient for \textcolor{yellow}{Langevin} dynamics\footfullcite{MR2427108}}
+  In \alert{dimension 1},
+  \( \lim_{\gamma \to 0} \gamma D^{\gamma} = D_{\rm und} \) and \( \lim_{\gamma \to \infty} \gamma D^{\gamma} = D_{\rm ovd}. \)
+  \begin{figure}[ht]
+      \centering
+      \includegraphics[width=0.5\linewidth,height=0.33\linewidth]{figures/scaling_diffusion_langevin.png}
+  \end{figure}
+
+  \textbf{\emph{Our aims in this work:}}
+  \begin{itemize}
+    \item How can we efficiently estimate the effective diffusion coefficient when \alert{$\gamma \ll 1$}?
+    \item How does the mobility scale as \alert{$\gamma \to 0$} in the multidimensional setting?
+  \end{itemize}
+\end{frame}
+
+
+\subsection{Efficient mobility estimation}%
+
+\begin{frame}
+  {Brief literature review}
+  % Consider the Langevin dynamics with $(\vect q_t, \vect p_t) \in (\real^{\alert{d}} \times \real^{\alert{d}})$:
+  % \begin{equation*}
+  %     \left\{
+  %       \begin{aligned}
+  %       & \d \vect q_t = \vect p_t \,\d t, \\
+  %       & \d \vect p_t = - \grad V (\vect q_t)  \, \d t - \gamma \vect p_t \, \d t + \sqrt{2 \gamma \beta^{-1}} \, \d \vect W_t.
+  %       \end{aligned}
+  %     \right.
+  % \end{equation*}
+  In dimension $> 1$, it \alert{does not hold} that
+  $\gamma D^{\gamma}_{\vect e} \xrightarrow[\gamma \to 0]{} D_{\rm und}$ when $V$ is \alert{non separable}, e.g.
+  \[
+    V(\vect q) = - \frac{1}{2} \Big( \cos(q_1) + \cos(q_2) \Big) - \alert{\delta} \cos(q_1) \cos(q_2)
+  \]
+
+  \textbf{Open question:}
+  how does $D^\gamma_{\vect e}$ behave when $\gamma \ll 1$ and \alert{$d = 2$}?
+  % \begin{block}
+    % {Open question: how does $D^\gamma_{\vect e}$ behave when $\gamma \ll 1$ and \alert{$d = 2$}?}
+
+  Various answers are given in the literature:
+  \begin{itemize}
+    \item
+      $D^{\gamma}_{\vect e} \propto \gamma^{-1/2}$ for specific potentials\footfullcite{chen1996surface};
+
+    \item
+      $D^{\gamma}_{\vect e} \propto \gamma^{-1/3}$ for specific potentials~\footfullcite{Braun02};
+
+    \item
+      $D^{\gamma}_{\vect e} \propto \gamma^{-\sigma}$ with $\sigma$ depending on the potential~\footfullcite{roussel_thesis}.
+  \end{itemize}
+  % \end{block}
+  \vspace{.5cm}
+\end{frame}
+
+\begin{frame}[label=continue]
+  {Numerical approaches for calculating the effective diffusion coefficient}
+  \begin{itemize}
+    \itemsep.5cm
+    \item \emph{Linear response approach}:
+        \begin{equation*}
+          D_{\vect e} = \lim_{\eta \to 0} \frac{1}{\beta \alert{\eta}} \expect_{\alert{\mu_\eta}} \, (\vect e^\t \vect p).
+        \end{equation*}
+        where $\mu_{\eta}$ is the invariant distribution of the system with external forcing.
+
+    \item \emph{Green--Kubo formula}: Since $-\mathcal L^{-1} = \int_{0}^{\infty} \e^{t \mathcal L} \, \d t$,
+      \begin{align*}
+        D_{\vect e} &= \int - \mathcal L^{-1}(\vect e^\t \vect p) \, (\vect \e^\t \vect p) \, \d \mu = \int_{0}^{\infty} \! \! \! \int \e^{t \mathcal L} (\vect e^\t \vect p) (\vect e^\t \vect p) \, \d \mu \, \d t  \\
+          &= \int_{0}^{\infty} \expect_{\mu}\bigl((\vect e^\t \vect p_0) (\vect e^\t \vect p_t)\bigr) \, \d t.
+      \end{align*}
+
+    \item \emph{Einstein's relation}:
+      \[
+        D_{\vect e} = \lim_{t \to \infty}  \frac{1}{2t} \expect_{\mu} \Bigl[ \bigl|\vect e^\t (\vect q_t - \vect q_0)\bigr|^2 \Bigr].
+      \]
+
+    \item Deterministic method, e.g. \emph{Fourier/Hermite Galerkin}, for the Poisson equation
+      \[
+        - \mathcal L \phi_{\vect e} = \vect e^\t \vect p, \qquad D_{\vect e} = \ip{\phi_{\vect e}}{p}.
+      \]
+  \end{itemize}
+\end{frame}
+
+% \begin{frame}
+%   {Fourier/Hermite Galerkin method for one-dimensional Langevin dynamics}
+%
+%   Saddle-point formulation\footfullcite{roussel2018spectral}:
+%   find $(\Phi_N, \alpha_N) \in V_N \times \real$ such that
+%   \begin{align}
+%        \notag
+%        - \Pi_N \, \mathcal L \, \Pi_N \alert{\Phi_N} + \alert{\alpha_N} u_N &= \Pi_N p, \\
+%        \label{eq:constraint}
+%         \ip{\Phi_N}{u_N} &= 0,
+%   \end{align}
+%   where
+%   \begin{itemize}
+%     \item $\Pi_N$ is the $L^2(\mu)$ projection operator on a finite-dimensional subspace $V_N$,
+%     \item $u_N = \Pi_N 1 / \norm{\Pi_N 1}$.
+%       Eq.~\eqref{eq:constraint} ensures that the system is \emph{well-conditioned}.
+%   \end{itemize}
+%
+%   \vspace{.2cm}
+%   For $V_N$, we use the following basis functions:
+%   \[
+%     e_{i,j} = {\left( Z \, \e^{\beta \left( H(q,p) + |z|^2 \right)} \right)}^{\frac{1}{2}} \, G_i(q) \, H_j(p), \qquad 0 \leq i,j \leq N,
+%   \]
+%   where $(G_i)_{i \geq 0}$ are \emph{trigonometric functions} and $(H_j)_{i \geq 0}$ are \emph{Hermite polynomials}.
+%
+%   $\rightarrow$ \alert{Impractical} in two or more spatial dimensions.
+% \end{frame}
+
+\begin{frame}
+  {Estimation of the effective diffusion coefficient from Einstein's relation}
+  Consider the following estimator of the effective diffusion coefficient $D_{\vect e}$:
+  \[
+    \emph{u(T) = \frac{\abs{\vect e^\t (\vect q_T - \vect q_0)}^2}{2T}}, \qquad (\vect q_0, \vect p_0) \sim \mu.
+  \]
+
+  \textbf{Bias of this estimator:}
+  \begin{align*}
+      \notag
+      \expect \bigl[u(T)\bigr]
+      % &= \int_{0}^{\infty} \ip{\e^{t \mathcal L}(\vect e^\t \vect p)}{\vect e^\t \vect p}  \d t
+      % - \int_{0}^{\infty} \ip{\e^{t \mathcal L} (\vect e^\t \vect p)}{\vect e^\t \vect p} \min\left\{1, \frac{t}{T}\right\} \, \d t \\
+      &= D_{\vect e} - \int_{0}^{\infty} \ip{\e^{t \mathcal L} (\vect e^\t \vect p)}{\vect e^\t \vect p} \min\left\{1, \frac{t}{T}\right\} \, \d t.
+  \end{align*}
+  Using the decay estimate for the semigroup\footfullcite{roussel2018spectral}
+  \[
+    \norm{\e^{t \mathcal L}}_{\mathcal B\left(L^2_0(\mu)\right)} \leq L \e^{- \ell \min\{\gamma, \gamma^{-1}\}t},
+  \]
+  we deduce
+  \[
+    \left\lvert \expect[u(T)] - D_{\vect e} \right\rvert \leq \frac{C \textcolor{red}{\max\{\gamma^2, \gamma^{-2}\}}}{T}.
+  \]
+\end{frame}
+
+\begin{frame}
+  {Variance of the estimator $u(T)$ for large $T$}
+  For $T \gg 1$,
+  it holds approximately that
+  \[
+    \frac{\vect e^\t (\vect q_T - \vect q_0)}{\sqrt{2T}} \sim \mathcal N(0, D_{\vect e})
+    \qquad \leadsto \qquad
+    u(T) = \frac{\abs{\vect e^\t (\vect q_T - \vect q_0)}^2}{2 D_{\vect e} T} \sim \chi^2 (1).
+  \]
+  Therefore, we deduce
+  \[
+    \lim_{T \to \infty} \var \bigl[u(T)\bigr] = 2 D_{\vect e}^2.
+  \]
+  The relative standard deviation (asymptotically as $T \to \infty$) is therefore
+  \[
+    \lim_{T \to \infty} \frac{\sqrt{\var \bigl[u(T)\bigr]}}{\expect \bigl[u(T)\bigr]} = \sqrt{2}
+    \qquad \leadsto \text{\emph{independent} of $\gamma$}.
+  \]
+
+  \begin{exampleblock}{Scaling of the  mean square error when using $J$ realizations}
+    Assuming an asymptotic scaling as $\gamma^{-\sigma}$ of $D_{\vect e}$, we have
+    \[
+      \forall \gamma \in (0, 1), \qquad
+      \frac{\rm MSE}{D_{\vect e}^2} \leq \frac{C}{\gamma^{4-2 \sigma} T^2} + \frac{2}{J}
+    \]
+  \end{exampleblock}
+\end{frame}
+
+% \subsection{Variance reduction using control variates}
+\begin{frame}
+  {Variance reduction using \textcolor{yellow}{control variates}}
+  Let $\phi_{\vect e}$ denote the solution to the \emph{Poisson equation}
+  \[
+    - \mathcal L \phi_{\vect e}(\vect q, \vect p) = \vect e^\t \vect p, \qquad \phi_{\vect e} \in L^2_0(\mu).
+  \]
+  and let $\psi_{\vect e}$ denote an approximation of $\phi_{\vect e}$.
+  By It\^o's formula,
+  we obtain
+  \[
+    \phi_{\vect e}(\vect q_T, \vect p_T) - \phi_{\vect e}(\vect q_0, \vect p_0)
+    = - \int_{0}^{T} \vect e^\t \vect p_t \, \d t + \sqrt{2 \gamma \beta^{-1}} \int_{0}^{T} \grad_{\vect p} \phi_{\vect e}(\vect q_t, \vect p_t) \cdot \d \vect W_t.
+  \]
+  Therefore
+  \begin{align*}
+    \vect e^\t (\vect q_T - \vect q_0)
+    &= \int_{0}^{T} \vect e^\t \vect p_t \, \d t \\
+    &\approx  - \psi_{\vect e}(\vect q_T, \vect p_T) + \psi_{\vect e}(\vect q_0, \vect p_0) + \sqrt{2 \gamma \beta^{-1}} \int_{0}^{T} \grad_{\vect p} \psi_{\vect e}(\vect q_t, \vect p_t) \cdot \d \vect W_t
+    =: \emph{\xi_T}.
+  \end{align*}
+  which suggests the \emph{improved estimator}
+  \[
+    v(T) = \frac{\abs{\vect e^\t (\vect q_T - \vect q_0)}^2}{2T} - \left( \frac{\abs{\xi_T}^2}{2T} - \lim_{T\to \infty}\expect \left[ \frac{\abs{\xi_T}^2}{2T} \right]  \right).
+  \]
+\end{frame}
+
+\begin{frame}
+  {Properties of the improved estimator}
+  \textbf{Smaller bias} if $-\mathcal L \psi_{\vect e} \approx \vect e^\t \vect p$:
+    \begin{align*}
+        \label{eq:basic_bound_bias}
+        \abs{\expect \bigl[ v(T) \bigr] - D^{\gamma}_{\vect e}}
+                &\leq  \frac{L \max\{\gamma^2, \gamma^{-2}\}}{T \ell^2 }  \,  \emph{\norm{\vect e^\t \vect p + \mathcal L \psi_{\vect e}}}  \left(\beta^{-1/2} + \norm{\mathcal L \psi_{\vect e}} \right).
+    \end{align*}
+
+  \textbf{Smaller variance}:
+    \begin{equation*}
+        \begin{aligned}[b]
+            \var \bigl[v(T)\bigr]
+            \leq
+            C &\left( T^{-1} \emph{\norm{\phi_{\vect e} - \psi_{\vect e}}[L^4(\mu)]}^2  + \gamma \emph{\norm{\grad_{\vect p} \phi_{\vect e} - \grad_{\vect p} \psi_{\vect e}}[L^4(\mu)]}^2 \right) \\
+              &\quad \times \left( T^{-1} \norm{\phi_{\vect e} + \psi_{\vect e}}[L^4(\mu)]^2  + \gamma \norm{\grad_{\vect p} \phi_{\vect e} + \grad_{\vect p} \psi_{\vect e}}[L^4(\mu)]^2 \right).
+        \end{aligned}
+    \end{equation*}
+
+
+    \textbf{Construction of $\psi_{\vect e}$ in the \alert{one-dimensional setting}}. We consider two approaches:
+    \begin{itemize}
+      \item Approximate the solution to the Poisson equation by a Galerkin method.
+      \item Use asymptotic result for the Poisson equation:
+        \[
+          \gamma \phi \xrightarrow[\gamma \to 0]{L^{2}(\mu)} \phi_{\rm und},
+        \]
+        which suggests letting $\psi = \phi_{\rm und} / \gamma$.
+    \end{itemize}
+\end{frame}
+
+\begin{frame}
+  {Construction of the approximate solution $\psi_{\vect e}$ \textcolor{yellow}{in dimension 2}}
+  We consider the potential
+  \[
+    V(\vect q) = - \frac{1}{2} \Big( \cos(q_1) + \cos(q_2) \Big) - \alert{\delta} \cos(q_1) \cos(q_2).
+  \]
+  \begin{itemize}
+    \item
+      For this potential, $\mat D$ is isotropic
+      $\leadsto$ sufficient to consider $\vect e = (1, 0)$,
+      \[
+        D_{(1,0)} = \ip{\phi_{(1, 0)}}{p_1},
+        \qquad - \mathcal L \phi_{(1,0)}(\vect q, \vect p) = p_1.
+      \]
+
+    \item
+      If \emph{$\delta = 0$}, then the solution is $\phi_{(1, 0)}(\vect q, \vect p) = \phi_{\rm 1D} (q_1, p_1)$,
+      where $\phi_{\rm 1D}$ solves
+      \[
+        - \mathcal L_{\rm 1D} \phi_{\rm 1D}(q, p) = p, \qquad V_{\rm 1D}(q) = \frac{1}{2} \cos (q).
+      \]
+
+    \item
+      We take $\emph{\psi_{(1,0)}(\vect q, \vect p) = \psi_{\rm 1D}(q_1, p_1)}$,
+      where $\psi_{\rm 1D} \approx \phi_{\rm 1D}$.
+  \end{itemize}
+\end{frame}
+
+\subsection{Numerical experiments}%
+\begin{frame}
+  {Numerical experiments for the one-dimensional case (1/2)}
+  \begin{figure}[ht]
+      \centering
+      \includegraphics[width=0.99\linewidth]{figures/underdamped_1d.pdf}
+  \end{figure}
+\end{frame}
+
+\begin{frame}
+  {Numerical experiments for the one-dimensional case (2/2)}
+  \begin{figure}[ht]
+    \centering
+    \includegraphics[width=0.99\linewidth]{figures/time.pdf}
+    \caption{Evolution of the sample mean and standard deviation, estimated from $J = 5000$ realizations for $\gamma = 10^{-3}$.}
+  \end{figure}
+\end{frame}
+
+\begin{frame}
+  {Performance of the control variates approach in dimension 2}
+  \begin{figure}[ht]
+    \centering
+    \includegraphics[width=0.49\linewidth]{figures/var-delta-galerkin.pdf}
+    \includegraphics[width=0.49\linewidth]{figures/var-delta-underdamped.pdf}
+    \label{fig:time_bias_deviation_2d}
+  \end{figure}
+  \begin{itemize}
+    \item Variance reduction is possible if $\abs{\delta}/\gamma \ll 1$;
+    \item Control variates are \alert{not very useful} when $\gamma \ll 1$ and $\delta$ is fixed.
+  \end{itemize}
+\end{frame}
+
+\begin{frame}
+  {Scaling of the mobility in dimension 2}
+  \begin{figure}[ht]
+    \centering
+    \includegraphics[width=0.9\linewidth]{figures/diffusion.pdf}
+    \label{fig:time_bias_variance_2d}
+  \end{figure}
+\end{frame}
+
+\begin{frame}{Summary and perspectives for future work}
+  In this talk, we presented
+  \begin{itemize}
+    \item a variance reduction approach for efficiently estimating the mobility;
+    \item numerical results showing that the scaling of the mobility is \emph{not universal}.
+  \end{itemize}
+
+  \textbf{Perspectives for future work:}
+  \begin{itemize}
+    \item Use alternative methods (PINNs, Gaussian processes) to solve the Poisson equation;
+    \item Improve and study variance reduction approaches for other transport coefficients.
+  \end{itemize}
+
+  \vspace{1cm}
+  \begin{center}
+    Thank you for your attention!
+  \end{center}
+\end{frame}
+
+\section{Optimal importance sampling for overdamped Langevin dynamics}
+
+\begin{frame}
+    {Collaborators}
+    \begin{figure}
+        \centering
+        \begin{minipage}[t]{.2\linewidth}
+            \centering
+            \raisebox{\dimexpr-\height+\ht\strutbox}{%
+              \includegraphics[height=\linewidth]{figures/collaborators/tony.jpg}
+            }
+        \end{minipage}\hspace{.03\linewidth}%
+        \begin{minipage}[t]{.21\linewidth}
+          Tony Lelièvre
+          \vspace{0.2cm}
+
+          \includegraphics[height=1.2cm,width=\linewidth,keepaspectratio]{figures/collaborators/enpc.png}
+          \flushleft \scriptsize
+          CERMICS \& Inria
+        \end{minipage}\hspace{.1\linewidth}%%
+        \begin{minipage}[t]{.2\linewidth}
+            \centering
+            \raisebox{\dimexpr-\height+\ht\strutbox}{%
+              \includegraphics[height=\linewidth]{figures/collaborators/gabriel.jpg}
+            }
+        \end{minipage}\hspace{.01\linewidth}%
+        \begin{minipage}[t]{.24\linewidth}
+          Gabriel Stoltz
+          \vspace{0.2cm}
+
+          \includegraphics[height=1.2cm,width=\linewidth,keepaspectratio]{figures/collaborators/enpc.png}
+          \flushleft \scriptsize
+          CERMICS \& Inria
+        \end{minipage}
+    \end{figure}
+
+    \vspace{.7cm}
+    \textbf{Outline:}
+    \vspace{.2cm}
+    \tableofcontents
+\end{frame}
+
+\subsection{Background and problem statement}
+
+\begin{frame}
+  {The sampling problem}
+
+  \begin{exampleblock}
+    {Objective of the sampling problem}
+    Calculate averages with respect to
+    \[
+      \mu = \frac{\e^{-V}}{Z},
+      \qquad Z = \int_{\torus^d} \e^{-V}.
+    \]
+    \vspace{-.4cm}
+  \end{exampleblock}
+
+  \vspace{-.2cm}
+  \textbf{Often in applications}:
+  \begin{itemize}
+    \item The dimension $d$ is large;
+    \item The normalization constant $Z$ is unknown;
+    \item We cannot generate i.i.d.\ samples from~$\mu$.
+  \end{itemize}
+
+  \textbf{Markov chain Monte Carlo (MCMC) approach}:
+  \[
+    \mu(f) \approx \mu^T (f) := \frac{1}{T} \int_{0}^{T} f(Y_t) \, \d t
+  \]
+  for a Markov process $(Y_t)_{t\geq 0}$ that is \emph{ergodic} with respect to~$\mu$.
+
+  \textbf{Example}: \emph{overdamped Langevin} dynamics
+  \[
+      \d Y_t = -\nabla V(Y_t) \, \d t + \sqrt{2} \, \d W_t,
+      \qquad Y_0 = y_0.
+  \]
+\end{frame}
+
+\begin{frame}
+  {Importance sampling in the MCMC context}
+  If $(X_t)_{t \geq 0}$ is a Markov process ergodic with respect to
+  \[
+      \mu_{U} = \frac{\e^{-V - U}}{Z_U},
+      \qquad Z_U = \int_{\torus^d} \e^{-V-U},
+  \]
+  then $\mu(f)$ may be approximated by
+  \begin{equation*}
+      \label{eq:estimator}
+      \mu^T_U(f) :=
+      \frac
+      {\displaystyle \frac{1}{T} \int_0^T (f \e^U)(X_t) \, \d t}
+      {\displaystyle \frac{1}{T} \int_0^T(\e^U)(X_t) \, \d t}.
+  \end{equation*}
+
+  \textbf{Asymptotic variance}:
+  Under appropriate conditions,
+  it holds that
+  \[
+      \sqrt{T} \bigl( \mu^T_U(f) - \mu(f)\bigr)
+      \xrightarrow[T \to \infty]{\rm Law} \mathcal N\bigl(0, \sigma^2_f[U]\bigr).
+  \]
+
+  \begin{exampleblock}
+    {Objective}
+    Find $U$ such that the asymptotic variance $\sigma^2_f[U]$ is minimized.
+  \end{exampleblock}
+\end{frame}
+
+\begin{frame}
+  {Background}
+\end{frame}
+
+
+\appendix
+
+\begin{frame}[noframenumbering,plain]
+  {Connection with the asymptotic variance of MCMC estimators}
+  \textbf{Ergodic theorem\footfullcite{MR885138}}:  for an observable $\varphi \in L^1(\mu)$,
+  \[
+    \widehat \varphi_t = \frac{1}{t} \int_{0}^{t} \varphi(\vect q_s, \vect p_s) \, \d s
+    \xrightarrow[t \to \infty]{a.s.} \expect_{\mu} \varphi.
+  \]
+
+  \textbf{Central limit theorem\footfullcite{MR663900}}:
+  If the following \emph{Poisson equation} has a solution $\phi \in L^2(\mu)$,
+  \[
+      - \mathcal L \phi = \varphi - \expect_{\mu} \varphi,
+  \]
+  then a central limit theorem holds:
+  \[
+    \sqrt{t} \bigl(\widehat \varphi_t - \expect_{\mu}\varphi\bigr)
+    \xrightarrow[t \to \infty]{\rm Law} \mathcal N(0, \sigma^2_{\varphi}),
+    \qquad
+    \sigma^2_{\varphi}
+    = \ip{\phi}{\varphi - \expect_{\mu} \varphi}.
+  \]
+
+  \textbf{Connection with effective diffusion}: Apply this result with $\varphi(\vect q, \vect p) = \vect e^\t \vect p$.
+\end{frame}
+
+\end{document}
+
+% vim: ts=2 sw=2