README.md

Optimal Stochastic Control and Neural Network-Based Solvers for the Committor Problem

This repository contains the implementation of paper Optimal control for sampling the transition path process and estimating rates.

We consider Ito's diffusion process with rare transitions between two predefined sets $A$ and $B$:

$$ dX_t = b(X_t) dt + \sigma(X_t) dW_t. $$

In particular, we focus on two instances of it: Langevin dynamics and overdamped Langevin dynamics in Collective Variables(CVs) governed by SDEs shown as follows

Langevin dynamics:

$$ \begin{cases} dX_t = m^{-1} P_t dt \\ dP_t = - (\nabla U + \gamma_f P_t) dt + \sqrt{2 \gamma_f \beta^{-1} m} dW_t, \end{cases} $$

Overdamped Langevin dynamics in CVs:

$$ d\xi_t = [-M(\xi_t)\nabla F(\xi_t) + \beta^{-1}\nabla \cdot M(\xi_t)]dt + \sqrt{2\beta^{-1}}M(\xi_t)^{\frac{1}{2}}dW_t. $$

Under the optimally controlled process

$$ dY_t = (b(Y_t) + \sigma(Y_t) \sigma^T(Y_t) v^*(Y_t)) dt + \sigma(Y_t) dW_t $$

such that $v^*(\cdot)$ satisfies

$$ \sigma^\top v^* = \sigma^\top \nabla \log q^+,$$

trajectories starting from boundary of $A$ goes to $B$ almost sure, where $q^+$ is the forward committor function.

The transition rate from $A$ to $B$ is defined as

$$ \nu_{AB} = \lim_{T \rightarrow \infty} \frac{N_{AB}}{T}$$

where $N_{AB}$ denotes the number of transitions from $A$ to $B$ in time interval $[0,T]$. The transition rate can be recovered via

$$ \nu_{AB} = \frac{\rho_{AB}}{\mathbb{E}[\tau_{AB}]} $$

where $\rho_{AB}$ is the reactive probability and $\mathbb{E}[\tau_{AB}]$ denotes the mean transition time from $A$ to $B$. The quantity $\mathbb{E}[\tau_{AB}]$ can be sampled from the optimal controlled process and $\rho_{AB}$ depends on the committor.

The forward committor function $q^+$ satisfies the boundary value problem:

$$ \begin{cases} b \cdot \nabla q^+ + \frac{1}{2} \sigma \sigma^T : \nabla \nabla q^+ = 0 & x \in \Omega \backslash (A \cup B)\\ q^+(x) = 0 & x \in \partial A\\ q^+(x) = 1 & x \in \partial B. \end{cases} $$

Committor functions are solved using NN-based solvers that are adapted from methods in the following papers:

Qianxiao Li, Bo Lin, and Weiqing Ren, Computing committor functions for the study of rare events using deep learning. In: The Journal of Chemical Physics 151 (2019)

M. Raissi, P. Perdikaris, and G.E. Karniadakis. Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial dif- ferential equations. In: Journal of Computational Physics 378 (2019).

George Em Karniadakis, Ioannis G. Kevrekidis, Lu Lu 5, Paris Perdikaris, Sifan Wang, and Liu Yang. [Physics-informed machine Learning](https://www.nature. com/articles/s42254-021-00314-5). In: Nature (2021).

FEM are also used as a comparison for the committor functions. For more details of the implementation, see https://github.com/mar1akc/transition_path_theory_FEM_distmesh

Requirements:

• python 3.7

Usage

Mueller potential in 2D

• Train NN-solver for committor function: Mueller/train_NN.py
• Compare results of committor using NN-solver and FEM; generate figures of committor values using NN: Mueller/evaluation_file.ipynb
• Compute transition rate and plot sampled trajectories under control: Mueller/nu_AB-optimal_control.ipynb

Lennard-Jones-7 in 2D

• Train NN-solver for committor function: LJ7_2D/train_NN.py
• Compare results of committor using NN-solver and FEM; generate figures of committor values using NN: LJ7_2D/evaluation.ipynb
• Compute transition rate with controlled dynamics; sample trajectories under control: LJ7_2D/nu_AB-optimal_control.ipynb
• Generate sampled trajectories under control: LJ7_2D/graph.ipynb
• Compute transition rate with unbiased dynamics in 14D: LJ7_2D/MALA_transitio_rate.py

Duffing Oscillator in 1D

• Train PINN-solver for committor function: Duffing/PINN_train.py
• Compare results of committor using NN-solver and FEM: Duffing/evaluation_PINN.ipynb
• Compute transition rate with controlled dynamics: Duffing/Enu_AB-optimal_control.ipynb
• Compute transition rate with unbiased dynamics: Duffing/nu_unbiased.py