A0662
Title: Deep-MacroFin: Informed equilibrium neural network for continuous time economic models
Authors: Goutham Gopalakrishna - Rotman School of Management, University of Toronto (Canada) [presenting]
Abstract: A comprehensive framework designed to solve partial differential equations is presented, 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 function approximation, elementary differential equations, and systems of differential equations, even in cases where the solutions may exhibit discontinuities. Moreover, it offers a more straightforward and user-friendly implementation than existing libraries.
