Work

Deep neural networks to correct sub-precision errors in CFD

Year: 2022

Type: article

Abstract: Information loss in numerical physics simulations can arise from various sources when solving discretised partial differential equations . In particular, errors related to numerical precision ( “sub-precision errors” ) can accumulate in the quantities of interest when the simulations are performed using low-precision 16-bit floating-point arithmetic compared to an equivalent 64-bit simulation. On the other hand, low-precision computation is less resource intensive than high-precision computation. Several machine learning techniques proposed recently have been successful in correcting errors due to coarse spatial discretisation . In this work, we extend these techniques to improve CFD simulations performed with low numerical precision. We quantify the precision-related errors accumulated in a Kolmogorov forced turbulence test case. Subsequently, we employ a Convolutional Neural Network together with a fully differentiable numerical solver performing 16-bit arithmetic to learn a tightly-coupled ML-CFD hybrid solver. 1 Compared to the 16-bit solver, we demonstrate the efficacy of the hybrid solver towards improving various metrics pertaining to the statistical and pointwise accuracy of the simulation.

Source: Applications in Energy and Combustion Science

DOI: 10.1016/j.jaecs.2022.100081

Authors: Akash Haridas, Nagabhushana Rao Vadlamani, Yuki Minamoto

Institutions: Indian Institute of Technology Madras, Tokyo Institute of Technology, Japan Science and Technology Agency

Language: English

Topic: Model Reduction and Neural Networks

Subfield: Statistical and Nonlinear Physics

Field: Physics and Astronomy

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,000