Machine learning for high-dimensional dynamic stochastic economies
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ML has the potential to help with sensitivity and uncertainty analysis, with finding numerical solutions for computationally expensive submodels, and assessing the validity of models.
“ML also has the potential to help with sensitivity and uncertainty analysis [386], with finding numerical solutions for computational expensive submodels [206,714], and assessing the validity of the models [556].”
From Tackling climate change with machine learning (2023) · cites this paper as [714]
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Lujan, A. (2026) · Journal of Economic Dynamics and Control · doi:10.1016/j.jedc.2026.105442
- Authors
- Scheidegger, S., & Bilionis, I.
- Affiliations
- University of Lausanne (CH)
- Purdue University West Lafayette (US)
- Corresponding author
- Scheidegger, Simon
- Venue
- Journal of Computational Science
- Publisher
- Elsevier BV
- Published
- 2019-04
- Volume
- 33
- Pages
- 68-82
- ISSN
- 1877-7503 (ISSN-L)
- OpenAlex
- W2593143642
- Cited by
- 72
- References
- 105
- Citations by year
- 2026: 7 · 2025: 7 · 2024: 9 · 2023: 7 · 2022: 13 · 2021: 7 · 2020: 7 · 2019: 8 · 2018: 4 · 2017: 2 · 2016: 1
- FWCI
- 3.43
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Locations
- Journal of Computational Science (publishedVersion)
- SSRN Electronic Journal (publishedVersion)
- IRIS (submittedVersion)
- SERVAL (Université de Lausanne) (submittedVersion)
- Funders
- Swiss National Supercomputing Centre (s555, s790, s885)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Gaussian Processes and Bayesian Inference
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Adaptive Dynamic Programming Control
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Economic theories and models
- Keywords
- Gaussian process regression, active subspaces, discrete-time dynamic programming, uncertainty quantification, epistemic uncertainty, Bayesian Gaussian mixture model, parameter uncertainty
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