Learn about the architecture and training process of a neural network-based dogfight agent developed using a custom RTDynamics simulation environment, and how it compares to a rule-based controller. Request your whitepaper here.
Dogfight between a traditional rule-based AI pilot and a neural network-controlled fighter trained with reinforcement learning (PPO algorithm).
The latest update to EWAWS adds four new example configurations for modeling artillery shells and rockets. These models include key aerodynamic and environmental effects relevant to projectile simulation and fire control applications.
We’re thrilled to announce a major milestone—RTD’s simulation model libraries are now successfully integrated with Python-based machine learning frameworks. This opens the door to powerful new possibilities in AI training for aerospace applications.
RTDynamics plug-in for VR-Forces 5.1.1d is now available to users under maintenance.
RTDynamics plug-in for VR-Forces 5.1.1c is now available to users under maintenance.
LAR is an Add-on to EWAWS. During simulation, weapon launching models (e.g. CGFs) can utilize data which is generated by the offline component of the LAR Add-on LAR Generator. The data allows the model to make a better launch timing. LAR supports air-to-air, air-to-ground and surface-to-air weapon launches. For more information please visit product page here.
Note
LAR add-on is available on both VR-Forces and C++ SDK.
EWAWS ESA Add-on allows simulation of active electronically scanned array (AESA) RADAR within EWAWS. For more information please visit product page here.
SAM site with AESA RADAR illumination with a time-shared beam.
Target illumination starts. Semi-active, radar guided missiles are launched. Missiles lack active radars. Instead they lock onto signals reflected by the targets and emitted by the illumination radar.