Optimal Prediction using Learning and Shape Optimization

TL;DR

This paper presents a method combining neural networks and shape optimization to design sensors with optimal shapes and locations for improved prediction accuracy in distributed parameter systems.

Contribution

It introduces a novel integrated approach for optimizing sensor shapes and placements using gradient-based methods within a neural network prediction framework.

Findings

Optimized sensor shapes and locations significantly reduce prediction error.

The combined approach improves prediction accuracy and reduces sensor costs.

Simulation results validate the effectiveness of the proposed method.

Abstract

This paper investigates the problem of optimal predictor design for distributed parameter systems using neural networks and shape optimization. Sensors with various shapes are placed on the domain of the distributed parameter system. Data provided by these sensors are fed into a re-constructor to generate a full state of the system. After that, a trained neural-network predictor produces a prediction of the state at future time steps. The cost of prediction is defined as the weighted sensor area plus the squared norm of the prediction error. The location and shape of a sensor influences the prediction cost as well as the predictor performance. With the aid of the gradient of the network with respect to its inputs, an outer optimization layer is augmented to find optimized locations and shapes of the sensors. Simulation results show good agreement between the predicted and the true states as well as a significant reduction in cost by sensors with optimized locations and shapes.