EconPapers    
Economics at your fingertips  
 

Optical neural engine for solving scientific partial differential equations

Yingheng Tang (), Ruiyang Chen, Minhan Lou, Jichao Fan, Cunxi Yu, Andrew Nonaka, Zhi Yao () and Weilu Gao ()
Additional contact information
Yingheng Tang: Lawrence Berkeley National Laboratory
Ruiyang Chen: The University of Utah
Minhan Lou: The University of Utah
Jichao Fan: The University of Utah
Cunxi Yu: University of Maryland
Andrew Nonaka: Lawrence Berkeley National Laboratory
Zhi Yao: Lawrence Berkeley National Laboratory
Weilu Gao: The University of Utah

Nature Communications, 2025, vol. 16, issue 1, 1-13

Abstract: Abstract Solving partial differential equations (PDEs) is the cornerstone of scientific research and development. Data-driven machine learning (ML) approaches are emerging to accelerate time-consuming and computation-intensive numerical simulations of PDEs. Although optical systems offer high-throughput and energy-efficient ML hardware, their demonstration for solving PDEs is limited. Here, we present an optical neural engine (ONE) architecture combining diffractive optical neural networks for Fourier space processing and optical crossbar structures for real space processing to solve time-dependent and time-independent PDEs in diverse disciplines, including Darcy flow equation, the magnetostatic Poisson’s equation in demagnetization, the Navier-Stokes equation in incompressible fluid, Maxwell’s equations in nanophotonic metasurfaces, and coupled PDEs in a multiphysics system. We numerically and experimentally demonstrate the capability of the ONE architecture, which not only leverages the advantages of high-performance dual-space processing for outperforming traditional PDE solvers and being comparable with state-of-the-art ML models but also can be implemented using optical computing hardware with unique features of low-energy and highly parallel constant-time processing irrespective of model scales and real-time reconfigurability for tackling multiple tasks with the same architecture. The demonstrated architecture offers a versatile and powerful platform for large-scale scientific and engineering computations.

Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.nature.com/articles/s41467-025-59847-3 Abstract (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-025-59847-3

Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/

DOI: 10.1038/s41467-025-59847-3

Access Statistics for this article

Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie

More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().

 
Page updated 2025-05-19
Handle: RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-025-59847-3