Abstract: A method for the autonomous construction and/or design of at least one component part of a component includes the step of determining a state (si) of the component part by a state module, wherein a state (si) is defined by parameters (pi) such as data and/or measured values of at least one property (ei) of the component part. The state (si) is transmitted to a reinforcement learning agent, which uses a reinforcement learning algorithm. A calculation function (ƒi) and/or an action (ai) is selected on the basis of a policy for a state (si) for the modification of at least one parameter (pi) by the reinforcement learning agent. A modeled value for the property (ei) is calculated using the modified parameter (pi). A new state (si+1) is calculated by an environment module on the basis of the modeled value for the property (ei).

Abstract: A method for modifying a component of a computer-generated model of a vehicle includes the steps of: performing a first simulation of a first accident of the model resulting in components of the model being deformed, producing a first video from the simulation, comparing frames of the first video with one another, calculating a first deformation sum from the comparison of the frames of the first video, modifying at least one of the components, performing a second simulation of a second accident of the model with the at least one modified component, producing a second video from the second simulation, comparing frames of the second video with one another, calculating a second deformation sum from the comparison of the frames of the second video, comparing the deformation sums, and rating the modification on the basis of the comparison of the deformation sums.

Abstract: A method for the autonomous construction and/or design of at least one component part of a component includes the step of determining a state (si) of the component part by a state module, wherein a state (si) is defined by parameters (pi) such as data and/or measured values of at least one property (ei) of the component part. The state (si) is transmitted to a reinforcement learning agent, which uses a reinforcement learning algorithm. A calculation function (ƒi) and/or an action (ai) is selected on the basis of a policy for a state (si) for the modification of at least one parameter (pi) by the reinforcement learning agent. A modeled value for the property (ei) is calculated using the modified parameter (pi). A new state (si+1) is calculated by an environment module on the basis of the modeled value for the property (ei).

Abstract: A heatable injector for fuel injection in an internal combustion engine, the injector having an injector housing, a fuel space which is situated inside the injector housing, an adjustable injector needle which is arranged in the injector housing for opening and closing a fuel discharge opening of the injector housing, and a heating device which is arranged inside the injector housing with a heating element for heating the fuel which is situated in the fuel space. The heating element is configured as a coating of a boundary face of the injector with respect to its fuel space, the coating being composed of a carbon nanoparticle material.

Abstract: A heatable injector for fuel injection in an internal combustion engine, the injector having an injector housing, a fuel space which is situated inside the injector housing, an adjustable injector needle which is arranged in the injector housing for opening and closing a fuel discharge opening of the injector housing, and a heating device which is arranged inside the injector housing with a heating element for heating the fuel which is situated in the fuel space. The heating element is configured as a coating of a boundary face of the injector with respect to its fuel space, the coating being composed of a carbon nanoparticle material.