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

• URL: http://arxiv.org/abs/2512.22248v1
• Date: Wed, 24 Dec 2025 01:32:04 GMT
• Title: Amortized Inference for Model Rocket Aerodynamics: Learning to Estimate Physical Parameters from Simulation
• Authors: Rohit Pandey, Rohan Pandey,
• Abstract summary: Accurate prediction of model rocket flight performance requires estimating aerodynamic parameters that are difficult to measure directly.<n>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.<n>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.
• Score: 3.6083839071040646
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Hybrid Model Predictive Control with Physics-Informed Neural Network for Satellite Attitude Control [2.7222301668137483]
  Reliable spacecraft attitude control depends on accurate prediction of attitude dynamics.<n>For spacecraft with complex dynamics, obtaining accurate physics-based models can be difficult, time-consuming, or computationally heavy.<n>This work explores Physics-Informed Neural Networks (PINNs) for modeling spacecraft attitude dynamics.
  arXiv  Detail & Related papers  (2026-02-17T19:08:48Z)
• ResAD: Normalized Residual Trajectory Modeling for End-to-End Autonomous Driving [64.42138266293202]
  ResAD is a Normalized Residual Trajectory Modeling framework.<n>It reframes the learning task to predict the residual deviation from an inertial reference.<n>On the NAVSIM benchmark, ResAD achieves a state-of-the-art PDMS of 88.6 using a vanilla diffusion policy.
  arXiv  Detail & Related papers  (2025-10-09T17:59:36Z)
• PI-WAN: A Physics-Informed Wind-Adaptive Network for Quadrotor Dynamics Prediction in Unknown Environments [3.4802474792943805]
  We introduce the Physics-Informed Wind-Adaptive Network (PI-WAN), which embeds physical constraints directly into the training process for robust quadrotor dynamics learning.<n>Specifically, PI-WAN employs a Temporal Convolutional Network (TCN) architecture that efficiently captures temporal dependencies from historical flight data.<n>By incorporating real-time prediction results into a model predictive control (MPC) framework, we achieve improvements in closed-loop tracking performance.
  arXiv  Detail & Related papers  (2025-07-01T14:48:22Z)
• Physics-guided Active Sample Reweighting for Urban Flow Prediction [75.24539704456791]
  Urban flow prediction is a nuanced-temporal modeling that estimates the throughput of transportation services like buses, taxis and ride-driven models. Some recent prediction solutions bring remedies with the notion of physics-guided machine learning (PGML) We develop a atized physics-guided network (PN), and propose a data-aware framework Physics-guided Active Sample Reweighting (P-GASR)
  arXiv  Detail & Related papers  (2024-07-18T15:44:23Z)
• Bridging the Sim-to-Real Gap with Bayesian Inference [53.61496586090384]
  We present SIM-FSVGD for learning robot dynamics from data. We use low-fidelity physical priors to regularize the training of neural network models. We demonstrate the effectiveness of SIM-FSVGD in bridging the sim-to-real gap on a high-performance RC racecar system.
  arXiv  Detail & Related papers  (2024-03-25T11:29:32Z)
• Pre-training on Synthetic Driving Data for Trajectory Prediction [61.520225216107306]
  We propose a pipeline-level solution to mitigate the issue of data scarcity in trajectory forecasting. We adopt HD map augmentation and trajectory synthesis for generating driving data, and then we learn representations by pre-training on them. We conduct extensive experiments to demonstrate the effectiveness of our data expansion and pre-training strategies.
  arXiv  Detail & Related papers  (2023-09-18T19:49:22Z)
• Machine learning enhanced real-time aerodynamic forces prediction based on sparse pressure sensor inputs [7.112725255953468]
  This paper presents a data-driven aerodynamic force prediction model based on a small number of pressure sensors. The model is tested on numerical and experimental dynamic stall data of a 2D NACA0015 airfoil, and numerical simulation data of dynamic stall of a 3D drone.
  arXiv  Detail & Related papers  (2023-05-16T06:15:13Z)
• Physics-Inspired Temporal Learning of Quadrotor Dynamics for Accurate Model Predictive Trajectory Tracking [76.27433308688592]
  Accurately modeling quadrotor's system dynamics is critical for guaranteeing agile, safe, and stable navigation. We present a novel Physics-Inspired Temporal Convolutional Network (PI-TCN) approach to learning quadrotor's system dynamics purely from robot experience. Our approach combines the expressive power of sparse temporal convolutions and dense feed-forward connections to make accurate system predictions.
  arXiv  Detail & Related papers  (2022-06-07T13:51:35Z)
• Airfoil's Aerodynamic Coefficients Prediction using Artificial Neural Network [0.0]
  Figuring out the right airfoil is a crucial step in the preliminary stage of any aerial vehicle design. This study compares different network architectures and training datasets in an attempt to gain insight as to how the network perceives the given airfoil geometries.
  arXiv  Detail & Related papers  (2021-09-24T19:07:19Z)
• Hybrid Physics and Deep Learning Model for Interpretable Vehicle State Prediction [75.1213178617367]
  We propose a hybrid approach combining deep learning and physical motion models. We achieve interpretability by restricting the output range of the deep neural network as part of the hybrid model. The results show that our hybrid model can improve model interpretability with no decrease in accuracy compared to existing deep learning approaches.
  arXiv  Detail & Related papers  (2021-03-11T15:21:08Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.