Abstract: Techniques that facilitate optimization of prototype and machine design within a three-dimensional fluid modeling environment are presented. For example, a system includes a modeling component, a machine learning component, and a graphical user interface component. The modeling component generates three-dimensional model of a mechanical device based on a library of stored data elements. The machine learning component predicts one or more characteristics of the mechanical device based on a first machine learning process associated with the three-dimensional model. The machine learning component also generates physics modeling data of the mechanical device based on the one or more characteristics of the mechanical device. The graphical user interface component provides, via a graphical user interface, a three-dimensional design environment associated with the three-dimensional model and a probabilistic simulation environment associated with optimization of the three-dimensional model.

Abstract: Techniques that facilitate optimization of prototype and machine design within a three-dimensional fluid modeling environment are presented. For example, a system includes a modeling component, a machine learning component, and a graphical user interface component. The modeling component generates three-dimensional model of a mechanical device based on a library of stored data elements. The machine learning component predicts one or more characteristics of the mechanical device based on a first machine learning process associated with the three-dimensional model. The machine learning component also generates physics modeling data of the mechanical device based on the one or more characteristics of the mechanical device. The graphical user interface component provides, via a graphical user interface, a three-dimensional design environment associated with the three-dimensional model and a probabilistic simulation environment associated with optimization of the three-dimensional model.

Abstract: Techniques that facilitate optimization of prototype and machine design within a three-dimensional fluid modeling environment are presented. For example, a system includes a modeling component, a machine learning component, and a graphical user interface component. The modeling component generates three-dimensional model of a mechanical device based on a library of stored data elements. The machine learning component predicts one or more characteristics of the mechanical device based on a first machine learning process associated with the three-dimensional model. The machine learning component also generates physics modeling data of the mechanical device based on the one or more characteristics of the mechanical device. The graphical user interface component provides, via a graphical user interface, a three-dimensional design environment associated with the three-dimensional model and a probabilistic simulation environment associated with optimization of the three-dimensional model.

Abstract: Techniques that facilitate optimization of prototype and machine design within a three-dimensional fluid modeling environment are presented. For example, a system includes a modeling component, a machine learning component, and a graphical user interface component. The modeling component generates three-dimensional model of a mechanical device based on a library of stored data elements. The machine learning component predicts one or more characteristics of the mechanical device based on a first machine learning process associated with the three-dimensional model. The machine learning component also generates physics modeling data of the mechanical device based on the one or more characteristics of the mechanical device. The graphical user interface component provides, via a graphical user interface, a three-dimensional design environment associated with the three-dimensional model and a probabilistic simulation environment associated with optimization of the three-dimensional model.