Spacetime Neural Network for High Dimensional Quantum Dynamics

08/04/2021
by   Jiangran Wang, et al.
0

We develop a spacetime neural network method with second order optimization for solving quantum dynamics from the high dimensional Schrödinger equation. In contrast to the standard iterative first order optimization and the time-dependent variational principle, our approach utilizes the implicit mid-point method and generates the solution for all spatial and temporal values simultaneously after optimization. We demonstrate the method in the Schrödinger equation with a self-normalized autoregressive spacetime neural network construction. Future explorations for solving different high dimensional differential equations are discussed.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
02/23/2023

Q-Flow: Generative Modeling for Differential Equations of Open Quantum Dynamics with Normalizing Flows

Studying the dynamics of open quantum systems holds the potential to ena...
research
12/24/2022

JDNN: Jacobi Deep Neural Network for Solving Telegraph Equation

In this article, a new deep learning architecture, named JDNN, has been ...
research
06/05/2023

Convex Relaxation for Fokker-Planck

We propose an approach to directly estimate the moments or marginals for...
research
12/19/2020

A deep learning method for solving Fokker-Planck equations

The time evolution of the probability distribution of a stochastic diffe...
research
05/27/2019

ODE^2VAE: Deep generative second order ODEs with Bayesian neural networks

We present Ordinary Differential Equation Variational Auto-Encoder (ODE^...
research
02/03/2020

Computing quantum dynamics in the semiclassical regime

The semiclassically scaled time-dependent multi-particle Schrödinger equ...
research
06/06/2021

Predicting Quantum Potentials by Deep Neural Network and Metropolis Sampling

The hybridizations of machine learning and quantum physics have caused e...

Please sign up or login with your details

Forgot password? Click here to reset