Compare Ansys VeloceRF vs. NeuralWing

Ansys VeloceRF accelerates the design process by significantly cutting down the time required to synthesize and model intricate spiral devices and transmission lines. Compiling the geometry of inductors or transformers takes just a matter of seconds, while modeling and analyzing them can be completed in just a few minutes. This software seamlessly integrates with top EDA platforms, creating layouts that are ready for tape-out. With Ansys VeloceRF, users can synthesize devices that tightly pack multiple components and lines, resulting in a more efficient silicon floorplan. Furthermore, analyzing the coupling effects among various inductive devices prior to detailed layout can decrease the overall design size and potentially eliminate the need for guard rings. The dimensions of inductors, along with crosstalk between them, can significantly influence the size of the die. Ansys VeloceRF assists in designing smaller devices by applying optimization criteria and geometry constraints, leading to enhanced performance. Additionally, it assesses the coupling between any number of inductors, optimizing both silicon area and inductor performance within the circuit context, ultimately contributing to a more efficient design process. By streamlining these aspects, Ansys VeloceRF empowers engineers to achieve their design goals more effectively.

NeuralWing serves as a cutting-edge model for real-time neural simulation and design optimization specifically tailored for transonic aircraft aerodynamics. It leverages the most comprehensive 3D transonic wing dataset, derived from 30,000 steady-state CFD simulations that span a 3D wing operating within the transonic regime, incorporating variations in four distinct geometry parameters and two different inflow conditions. By utilizing Emmi’s AB-UPT surrogate model, which has been meticulously trained on this extensive dataset, NeuralWing empowers users to effortlessly alter wing geometries, conduct optimizations, and enhance aerodynamic efficiency within mere seconds. The model is designed to facilitate transonic 3D wing simulations, accommodating variations in geometry and inflow, while offering real-time inference and optimization of design parameters. Users input a geometry mesh in STL format along with speed and angle of attack, and in return, they receive outputs that include pressure, friction, velocity fields, and integral forces such as lift and drag. Geometry meshes are generated dynamically in response to four design parameters, employing a differentiable approach that allows for swift assessment of design modifications. Furthermore, NeuralWing boasts an impressive accuracy rate of 99.5%, making it an invaluable tool for aerodynamics research and development. This level of precision ensures that engineers can trust the results as they iterate on their designs.