Pith Number

pith:RY5323JI

pith:2026:RY5323JI5DH4IGJ4JZWBTQWPDR

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Deep Learning for Solving and Estimating Dynamic Models in Economics and Finance

Simon Scheidegger

Deep learning methods solve and estimate high-dimensional dynamic stochastic models in economics and finance by embedding equilibrium conditions into neural-network training.

arxiv:2605.14493 v1 · 2026-05-14 · econ.GN · q-fin.EC

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Record completeness

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3 Author claim open · sign in to claim

4 Citations open

5 Replications open

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The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same current state with the deterministic merge algorithm.

Claims

C1strongest claim

Deep learning methods such as Deep Equilibrium Nets, Physics-Informed Neural Networks, deep surrogate models, and Gaussian-process dynamic programming can solve and estimate high-dimensional dynamic stochastic models in economics and finance that strain classical tensor-product grid methods.

C2weakest assumption

That the neural-network approximations remain accurate and stable when applied to the equilibrium conditions and dynamics of the high-dimensional models described, without introducing material bias or convergence failures.

C3one line summary

The paper surveys deep learning methods such as Deep Equilibrium Nets and Physics-Informed Neural Networks for solving and estimating high-dimensional dynamic stochastic models in economics and finance.

References

13 extracted · 13 resolved · 4 Pith anchors

[1] Achdou, Y., Han, J., Lasry, J.-M., Lions, P.-L., and Moll, B. (2022). Income and wealth distribution in macroeconomics: A continuous-time approach.The Review of Economic Studies, 89(1):45–86. Adjemian 2022

[2] Solving Nonlinear and High-Dimensional Partial Differential Equations via Deep Learning 2001 · arXiv:1811.08782

[3] Bayer, C. and Luetticke, R. (2020). Solving discrete time heterogeneous agent models with aggre- gate risk and many idiosyncratic states by perturbation.Quantitative Economics, 11(4):1253– 2020

[4] Belkin, M., Hsu, D., Ma, S., and Mandal, S. (2019). Reconciling modern machine-learning practice and the classical bias–variance trade-off.Proceedings of the National Academy of Sciences, 116(32):1584 2019

[5] Global solu- tions to master equations for continuous time heteroge- neous agent macroeconomic models.arXiv:2406.13726 2020

Receipt and verification

First computed	2026-05-17T23:39:06.409125Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

8e3bbd6d28e8cfc4193c4e6c19c2cf1c6eeda8093d4160ac42a976073177bcfb

Aliases

arxiv: 2605.14493 · arxiv_version: 2605.14493v1 · doi: 10.48550/arxiv.2605.14493 · pith_short_12: RY5323JI5DH4 · pith_short_16: RY5323JI5DH4IGJ4 · pith_short_8: RY5323JI

Agent API

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/RY5323JI5DH4IGJ4JZWBTQWPDR \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 8e3bbd6d28e8cfc4193c4e6c19c2cf1c6eeda8093d4160ac42a976073177bcfb

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "e62f1a76c3bc6086ac66596db20357448a1eaf1170d87f930cbd3e4f61b38691",
    "cross_cats_sorted": [
      "q-fin.EC"
    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "econ.GN",
    "submitted_at": "2026-05-14T07:33:36Z",
    "title_canon_sha256": "7a459cb7385667779e05ac6b0757a5c08f99d74716d1fcd0d1169e65b63d8657"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.14493",
    "kind": "arxiv",
    "version": 1
  }
}