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Amortized Inference for Model Rocket Aerodynamics: Learning to Estimate Physical Parameters from Simulation

Rohit Pandey, Rohan Pandey

TL;DR

The paper tackles the challenge of estimating aerodynamic parameters for model rockets from extremely sparse data by casting parameter estimation as amortized inference. It trains a neural network ensemble on synthetic flights generated by a physics simulator to invert the forward model and predict $C_d$ and a thrust correction factor $\alpha$ from a single apogee measurement plus configuration features, achieving zero-shot transfer to real flights. On eight real flights, the method yields a mean absolute error of $12.3$ m for apogee (38% better than the OpenRocket baseline), while revealing a systematic positive bias likely due to unmodeled real-world effects. The work provides practical gains for amateur rocketry and points to future improvements in simulator fidelity and robustness via domain randomization and richer observational data.

Abstract

Accurate prediction of model rocket flight performance requires estimating aerodynamic parameters that are difficult to measure directly. Traditional approaches rely on computational fluid dynamics or empirical correlations, while data-driven methods require extensive real flight data that is expensive and time-consuming to collect. We present a simulation-based amortized inference approach that trains a neural network on synthetic flight data generated from a physics simulator, then applies the learned model to real flights without any fine-tuning. Our method learns to invert the forward physics model, directly predicting drag coefficient and thrust correction factor from a single apogee measurement combined with motor and configuration features. In this proof-of-concept study, we train on 10,000 synthetic flights and evaluate on 8 real flights, achieving a mean absolute error of 12.3 m in apogee prediction - demonstrating promising sim-to-real transfer with zero real training examples. Analysis reveals a systematic positive bias in predictions, providing quantitative insight into the gap between idealized physics and real-world flight conditions. We additionally compare against OpenRocket baseline predictions, showing that our learned approach reduces apogee prediction error. Our implementation is publicly available to support reproducibility and adoption in the amateur rocketry community.

Amortized Inference for Model Rocket Aerodynamics: Learning to Estimate Physical Parameters from Simulation

TL;DR

The paper tackles the challenge of estimating aerodynamic parameters for model rockets from extremely sparse data by casting parameter estimation as amortized inference. It trains a neural network ensemble on synthetic flights generated by a physics simulator to invert the forward model and predict and a thrust correction factor from a single apogee measurement plus configuration features, achieving zero-shot transfer to real flights. On eight real flights, the method yields a mean absolute error of m for apogee (38% better than the OpenRocket baseline), while revealing a systematic positive bias likely due to unmodeled real-world effects. The work provides practical gains for amateur rocketry and points to future improvements in simulator fidelity and robustness via domain randomization and richer observational data.

Abstract

Accurate prediction of model rocket flight performance requires estimating aerodynamic parameters that are difficult to measure directly. Traditional approaches rely on computational fluid dynamics or empirical correlations, while data-driven methods require extensive real flight data that is expensive and time-consuming to collect. We present a simulation-based amortized inference approach that trains a neural network on synthetic flight data generated from a physics simulator, then applies the learned model to real flights without any fine-tuning. Our method learns to invert the forward physics model, directly predicting drag coefficient and thrust correction factor from a single apogee measurement combined with motor and configuration features. In this proof-of-concept study, we train on 10,000 synthetic flights and evaluate on 8 real flights, achieving a mean absolute error of 12.3 m in apogee prediction - demonstrating promising sim-to-real transfer with zero real training examples. Analysis reveals a systematic positive bias in predictions, providing quantitative insight into the gap between idealized physics and real-world flight conditions. We additionally compare against OpenRocket baseline predictions, showing that our learned approach reduces apogee prediction error. Our implementation is publicly available to support reproducibility and adoption in the amateur rocketry community.
Paper Structure (30 sections, 5 equations, 2 figures, 2 tables, 1 algorithm)

This paper contains 30 sections, 5 equations, 2 figures, 2 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Research Motivation.
  3. Proposed Methodology.
  4. Contributions.
  5. Related Work
  6. Simulation-Based Inference.
  7. Physics-Informed Machine Learning.
  8. System Identification in Aerospace.
  9. Rocket Flight Physics
  10. Aerodynamic and Thrust Models
  11. Model Assumptions and Limitations
  12. Forward Simulation
  13. Methodology
  14. Problem Formulation and Identifiability
  15. Synthetic Data Generation
  16. ...and 15 more sections

Figures (2)

  • Figure 1: Training dynamics. Left: Loss converges within 40 epochs. Right: Parameter estimation accuracy stabilizes at $C_d$ MAE $\approx 0.09$.
  • Figure 2: Parity plots on synthetic test data. Left: Predicted vs. true drag coefficient. Right: Predicted vs. true thrust factor. Dashed line indicates perfect prediction.