Accurate estimation of aircraft takeoff weight (TOW) is essential for air traffic management, emissions modeling, and trajectory optimization, yet this information is rarely available in operational surveillance data. Existing statistical approaches achieve high accuracy but depend on extensive proprietary feature sets and large, region-specific training datasets, which limits their generalizability. This paper introduces a physics-based methodology that combines nonlinear optimal control with statistical learning to estimate TOW from only a small number of openly available flight parameters. Using the OpenAP performance model and the OpenAP.top trajectory optimizer, we generate a synthetic fuel-optimal dataset spanning 36 aircraft types over systematically varied takeoff weights, flight distances, and air temperatures. This dataset provides a controlled and physically consistent basis for training TOW estimation models without relying on sensitive data. Two regression approaches are evaluated across four compact feature sets built from two to four inputs: altitude, distance, true airspeed, and temperature. Validated against about 390,000 real flights from the EUROCONTROL Performance Review Commission 2024 Data Challenge, the aircraft type-specific models achieve a global mean absolute percentage error (MAPE) of 6.19 %. Purely statistical models trained on the challenge dataset reach 4.12 % MAPE, but their accuracy degrades substantially for underrepresented aircraft types, reflecting overfitting to dataset biases. Sensitivity analyses show that temperature provides modest gains (0.03–3.7 % MAPE reduction), while removing wind information increases error by at most 2.75 % MAPE. Two-feature models using only altitude and distance remain competitive, with MAPE increasing by just 0–6.4 % when weather data is unavailable. The methodology represents a practical tradeoff: although it does not match the numerical accuracy of models trained directly on operational data, it offers stronger reproducibility, transparency, and broader applicability, requiring only two to four open-source features, supporting aircraft types with limited or no labeled real-world data, and providing ready-to-use linear coefficients for quick approximate estimation without any software dependency.

The mass of an aircraft is crucial for performance-related studies, such as predicting flight trajectories and analyzing flight emissions. In these studies, the flight trajectories are often reconstructed using a point-mass aircraft performance model combined with flight profiles from surveillance data and take-off mass information. However, airlines do not usually disclose take-off mass information, considering its sensitive nature. Thus, aircraft masses often need to be assumed or estimated. This paper presents a simple and computationally effective approach for estimating take-off mass using only open data and models. We explore the strong correlation between take-off mass, flight distance, cruise altitude, and partially, the airspeed during the cruise. The main idea is to generate fuel-optimal trajectories with known masses and distances, and then compare them with actual flight data. The optimal trajectories are generated using the open aircraft performance and optimization library. By assuming that actual flights follow quasi-fuel-optimal trajectories, the take-off mass of a flight can be estimated based on simple regression models trained on the optimal trajectory dataset. This open-loop take-off mass estimation approach requires no proprietary information from aircraft manufacturers or airlines. We verified the model with an anonymized dataset containing actual A320 flights with known take-off mass. Our two- and three-feature multi-linear models yield mean absolute percentage errors of 5.95 % and 4.89 %, respectively. This study is another step forward in open science and a contribution to the aircraft trajectory studies.