Surrogate model

About: Surrogate model is a research topic. Over the lifetime, 5019 publications have been published within this topic receiving 77441 citations.

Multiobjective optimization for crash safety design of vehicles using stepwise regression model

Abstract: In automotive industry, structural optimization for crashworthiness criteria is of special importance. Due to the high nonlinearities, however, there exists substantial difficulty to obtain accurate continuum or discrete sensitivities. For this reason, metamodel or surrogate model methods have been extensively employed in vehicle design with industry interest. This paper presents a multiobjective optimization procedure for the vehicle design, where the weight, acceleration characteristics and toe-board intrusion are considered as the design objectives. The response surface method with linear and quadratic basis functions is employed to formulate these objectives, in which optimal Latin hypercube sampling and stepwise regression techniques are implemented. In this study, a nondominated sorting genetic algorithm is employed to search for Pareto solution to a full-scale vehicle design problem that undergoes both the full frontal and 40% offset-frontal crashes. The results demonstrate the capability and potential of this procedure in solving the crashworthiness design of vehicles.

The Transferability Approach: Crossing the Reality Gap in Evolutionary Robotics

Abstract: The reality gap, which often makes controllers evolved in simulation inefficient once transferred onto the physical robot, remains a critical issue in evolutionary robotics (ER). We hypothesize that this gap highlights a conflict between the efficiency of the solutions in simulation and their transferability from simulation to reality: the most efficient solutions in simulation often exploit badly modeled phenomena to achieve high fitness values with unrealistic behaviors. This hypothesis leads to the transferability approach, a multiobjective formulation of ER in which two main objectives are optimized via a Pareto-based multiobjective evolutionary algorithm: 1) the fitness; and 2) the transferability, estimated by a simulation-to-reality (STR) disparity measure. To evaluate this second objective, a surrogate model of the exact STR disparity is built during the optimization. This transferability approach has been compared to two reality-based optimization methods, a noise-based approach inspired from Jakobi's minimal simulation methodology and a local search approach. It has been validated on two robotic applications: 1) a navigation task with an e-puck robot; and 2) a walking task with a 8-DOF quadrupedal robot. For both experimental setups, our approach successfully finds efficient and well-transferable controllers only with about ten experiments on the physical robot.

Variance-based sensitivity analysis of model outputs using surrogate models

Abstract: If a computer model is run many times with different inputs, the results obtained can often be used to derive a computationally cheaper approximation, or surrogate model, of the original computer code. Thereafter, the surrogate model can be employed to reduce the computational cost of a variance-based sensitivity analysis (VBSA) of the model output. Here, we draw attention to a procedure in which an adaptive sequential design is employed to derive surrogate models and estimate sensitivity indices for different sub-groups of inputs. The results of such group-wise VBSAs are then used to select inputs for a final VBSA. Our procedure is particularly useful when there is little prior knowledge about the response surface and the aim is to explore both the global variability and local nonlinear features of the model output. Our conclusions are based on computer experiments involving the process-based river basin model INCA-N, in which outputs like the average annual riverine load of nitrogen can be regarded as functions of 19 model parameters.