Data and code underlying the publication: Neural oscillators for generalization of physics-informed machine learning
doi: 10.4121/da6fadb7-843b-4c86-a231-884d88e64868
CC BY 4.0
### Research Objective
The primary objective of this research is to enhance the generalization of physics-informed machine learning (PIML) models by integrating them with neural oscillators. The goal is to improve the accuracy of these models in predicting solutions to partial differential equations (PDEs) beyond the training domain.
### Type of Research
This research is applied and experimental. It focuses on developing and validating a new methodological approach to enhance the generalization capabilities of PIML models through a series of numerical experiments on various nonlinear and high order PDEs.
### Method of Data Collection
The data used for validating numerical experiments are closed form analytic solution and physics-informed method is utilized to simulate the dataset. Both are explicitly mention in the python notebooks. The experiments are conducted on time-dependent nonlinear PDEs, including the viscous Burgers equation, Allen-Cahn equation, nonlinear Schrödinger equation, Euler-Bernoulli beam equation, and a 2D Kovasznay flow.
### Type of Data/codes
1. All implementation are done using jupyter notebooks (.ipynb) or .py.
2. .mat files are analytical solution generated using PINN simulation.
3. (.jpeg), (.pdf) are figures which are used in the main manuscript.
- 2024-06-05 first online, published, posted
DATA
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67,129,861 bytesMD5:
851ee6841f1cc72946fc5a7a27d8880cAAAI_Codes.zip - download all files (zip)
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