This repository contains code to generate all figures and output for "Taming the Curse of Dimensionality: Quantitative Economics with Deep Learning" (Jesús Fernández-Villaverde, Galo Nuño, Jesse Perla).

It runs on all major operating systems and does not require any accelerators (e.g., GPUs). While you can use any Python environment manager, we recommend uv, a faster and reproducible alternative to Conda, albeit with incomplete support for challenging binary dependencies.

Replication Instructions

1. Install uv, which is usually a one-line install, such as on macOS and Linux:

   curl -LsSf https://astral.sh/uv/install.sh | sh

   • On Windows: winget install --id=astral-sh.uv -e or powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"
   • If you have any installation issues, see the docs for troubleshooting
2. Synchronize the environment. If you directly use uv run python as below, this step is done automatically.

   uv sync

3. Run the main script using one of two approaches:
   • If you activated the environment: See here for activation details, then run:

     python generate_paper_figures_pytorch.py

   • If you didn't activate: Use uv run to automatically activate and then run:

     uv run python generate_paper_figures_pytorch.py

All output is in the .figures directory, including generated figures and a results.json that summarizes numerical values used in the paper.

Note: On the first run, most time is spent in the baseline solver using classic methods. Afterwards, results are cached and subsequent runs are much faster.

Quick start on Linux or macOS:

curl -LsSf https://astral.sh/uv/install.sh | sh
uv run python generate_paper_figures_pytorch.py

Additional Instructions

The following section provides a few variations on the setup and execution.

Activation in VS Code

Ensure you activate the Python environment (i.e., the .venv or conda virtual environment):

• In VS Code, use >Python: Select Interpreter to select the local .venv
• Outside VS Code, a platform-specific command will activate .venv in your terminal.

Setup with Conda

If you prefer to use conda for your environment, then you can use the provided requirements.txt

conda create -n taming python=3.13
conda activate taming
pip install -r requirements.txt

Self-contained Execution

You can run the code for a particular set of parameters directly on the commandline. Again, it will cache the baseline results for any particular set of arguments so that subsequent runs are much faster.

The execution with the default arguments (as used in the paper and figures) is simply:

python stochastic_growth_pytorch.py

However, you can change parameters on the commandline. A few variations:

python stochastic_growth_pytorch.py --k_0_multiplier=0.9 --seed=53
python stochastic_growth_pytorch.py --mlp_width=128
python stochastic_growth_pytorch.py --opt_set.max_iter=15 --data_set.train_T=30
python stochastic_growth_pytorch.py --base_solver_set.num_z_points=41 --base_solver_set.num_k_points=100

Finally, to conduct experiments you can import the stochastic_growth_pytorch module and call stochastic_growth with whatever arguments you wish. See the generate_paper_figures_pytorch.py file for examples of how to do this.

Summary of Files

• pyproject.toml and associated uv.lock contain the package dependencies and versions used in the experiments. The requirements.txt was generated by uv pip freeze > requirements.txt for compatibility with pip and conda.
• stochastic_growth_baseline_pytorch.py: Solves the stochastic growth model using a standard algorithm (Euler iteration with linear interpolation) to provide a baseline for comparison.
  • This is by far the slowest part of the code as Python (even with Pytorch or JAX) is not especially convenient for the classic numerical methods used in our baseline.
• stochastic_growth_pytorch.py: Solves the stochastic growth model using deep learning as described in the paper.
  • It calls the stochastic_growth_baseline_pytorch module to calculate the relative error versus the baseline.
• generate_paper_figures_pytorch.py: Generates all figures and numerical results used in the paper.
  • It imports and calls stochastic_growth_pytorch.py and contains a summary of all default parameters for easy reference.
  • It saves all output to the .figures directory, including a results.json file that summarizes numerical results.