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Deep-MacroFin: Informed Equilibrium Neural Network for Continuous Time Economic Models

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  • Yuntao Wu
  • Jiayuan Guo
  • Goutham Gopalakrishna
  • Zisis Poulos

Abstract

In this paper, we present Deep-MacroFin, a comprehensive framework designed to solve partial differential equations, with a particular focus on models in continuous time economics. This framework leverages deep learning methodologies, including conventional Multi-Layer Perceptrons and the newly developed Kolmogorov-Arnold Networks. It is optimized using economic information encapsulated by Hamilton-Jacobi-Bellman equations and coupled algebraic equations. The application of neural networks holds the promise of accurately resolving high-dimensional problems with fewer computational demands and limitations compared to standard numerical methods. This versatile framework can be readily adapted for elementary differential equations, and systems of differential equations, even in cases where the solutions may exhibit discontinuities. Importantly, it offers a more straightforward and user-friendly implementation than existing libraries.

Suggested Citation

  • Yuntao Wu & Jiayuan Guo & Goutham Gopalakrishna & Zisis Poulos, 2024. "Deep-MacroFin: Informed Equilibrium Neural Network for Continuous Time Economic Models," Papers 2408.10368, arXiv.org, revised Oct 2024.
    • Handle: RePEc:arx:papers:2408.10368
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    File URL: http://arxiv.org/pdf/2408.10368
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    1. Goutham Gopalakrishna, 2021. "ALIENs and Continuous Time Economies," Swiss Finance Institute Research Paper Series 21-34, Swiss Finance Institute.
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