1. SMS: Spiking marching scheme for efficient long time integration of differential equations.
- Author
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Zhang, Qian, Kahana, Adar, Karniadakis, George Em, and Stinis, Panos
- Subjects
- *
ARTIFICIAL neural networks , *PARTIAL differential equations , *TIME integration scheme , *ARTIFICIAL intelligence , *DIFFERENTIAL equations - Abstract
We propose a Spiking Neural Network (SNN)-based explicit numerical scheme for long time integration of time-dependent Ordinary and Partial Differential Equations (ODEs, PDEs). The core element of the method is a SNN, trained to use spike-encoded information about the solution at previous timesteps to predict spike-encoded information at the next timestep. After the network has been trained, it operates as an explicit numerical scheme that can be used to compute the solution at future timesteps, given a spike-encoded initial condition. A decoder is used to transform the evolved spiking-encoded solution back to function values. We present results from numerical experiments of using the proposed method for ODEs and PDEs of varying complexity. • Developing the Spiking Marching Scheme – SNN based integrator. • Experiments for extrapolation. • Chaotic systems. • Exploring many different encoders. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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