ANSYS, Inc.
Processor-implemented systems using neural networks for simulating high quantile behaviors in physical systems

Abstract:

Systems and methods are provided for simulating quantile behavior of a physical system. A plurality of parameter samples to a physical system are accessed and a subset of the parameter samples are identified, each of the plurality of parameter samples including a variation of parameters for the physical system. The physical system is simulated based on the subset of the parameter samples to generate simulation results, each of the subset of the parameter samples corresponding to a respective one of the simulation results. A neural network is trained to predict the simulation results based on the subset of the parameter samples. Simulation results are predicted for the plurality of parameter samples based on the neural network which has been trained, each of the predicted simulation results corresponding to a respective one of the plurality of parameter samples, and an indicator is generated indicating a quantile simulation result of the physical system according to an ordering relationship among the plurality of simulation results.