CITATION.cff

cff-version: 1.2.0
title: "Deep Learning for Solving and Estimating Dynamic Models in Economics and Finance"
message: "If you use this course, please cite it as below."
type: software
authors:
  - family-names: Scheidegger
    given-names: Simon
    affiliation: University of Lausanne
repository-code: "https://github.com/sischei/Deep_Learning_for_Solving_And_Estimating_Dynamic_Economic_Models"
url: "https://github.com/sischei/Deep_Learning_for_Solving_And_Estimating_Dynamic_Economic_Models"
abstract: >-
  An open-source 18-lecture self-study course for PhD students in
  computational and quantitative economics and finance. Covers deep
  equilibrium nets, physics-informed neural networks, Gaussian processes,
  deep surrogate models, structural estimation via SMM, and deep
  uncertainty quantification for integrated assessment models, with a
  cross-cutting toolkit on agentic research-coding workflows.
keywords:
  - deep learning
  - dynamic stochastic general equilibrium
  - heterogeneous agents
  - physics-informed neural networks
  - Gaussian processes
  - structural estimation
  - integrated assessment models
  - climate economics
license: MIT
identifiers:
  - type: other
    value: "arXiv:2605.14493"
    description: arXiv eprint
  - type: url
    value: "https://arxiv.org/abs/2605.14493"
    description: arXiv manuscript
  - type: doi
    value: "10.2139/ssrn.6758340"
    description: SSRN DOI
  - type: url
    value: "https://ssrn.com/abstract=6758340"
    description: SSRN manuscript
preferred-citation:
  type: article
  title: "Deep Learning for Solving and Estimating Dynamic Models in Economics and Finance"
  authors:
    - family-names: Scheidegger
      given-names: Simon
      affiliation: University of Lausanne
  year: 2026
  date-published: "2026-05-13"
  journal: "arXiv preprint arXiv:2605.14493"
  url: "https://arxiv.org/abs/2605.14493"