ThermoNeRF can be adapted for use with different frameworks by ensuring compatibility and integration with the specific requirements of those frameworks. This adaptation may involve modifying the network architecture, data preprocessing steps, loss functions, or training strategies to align with the new framework's constraints and objectives. For instance, if integrating ThermoNeRF into a real-time application, optimizations for speed and efficiency would be crucial. Additionally, adapting ThermoNeRF to work seamlessly with other neural network architectures or libraries may require adjustments in input/output formats and model interfaces.