Recent Developing a Novel Parameter Estimation Method for an Agent-Based Model in Immune System Simulation Using History Matching: A Case Study on Influenza a Virus Infection| Chapter 4 | Recent Research Advances in Biology Vol. 10

 Agent-based models (ABM) have been widely employed for immune system simulation because they may provide a natural and flexible description of nonlinear dynamic behaviour of complex systems. However, including experimental data into ABM is critical for obtaining an adequate estimation for the model's main parameters. A systematic strategy for immune system simulation is proposed in this research by combining the ABM and regression method within the context of history matching. During the operation, a novel parameter estimate approach is suggested that incorporates the experiment data for the simulator ABM. To begin, we use ABM as a simulator to model the situation. system of defence Then, utilising the ABM's input and output data, the dimension-reduced type generalised additive model (GAM) is used to train a statistical regression model and serve as an emulator during history matching. Next, we introduce an implausible measure to exclude the implausible input values, reducing the parameter input space. Finally, the particle swarm optimization algorithm (PSO) is used to estimate model parameters by fitting the data. system of defences Then, utilising the ABM's input and output data, the dimension-reduced type generalised additive model (GAM) is used to train a statistical regression model and act as an emulator during history matching. Following that, we provide an implausible measure to eliminate the implausible input values, reducing the parameter input space. Finally, the particle swarm optimization technique (PSO) is used to estimate model parameters by fitting the data to the model.  Among the non-implausible input values, there is data from an experiment. The performance of our suggested method is demonstrated using a genuine Influenza A Virus (IAV) data set, and the results show that the proposed method not only has good fitting and prediction accuracy, but also has good computational efficiency.

Author (s) Details

Le Zhang
College of Computer Science, Sichuan University, Chengdu 610065, China.

Tingting Li
College of Mathematics and Statistics, Southwest University, Chongqing 400715, China.

View Book :-  https://stm.bookpi.org/RRAB-V10/article/view/2134

An Approach of Optimization Techniques for History Matching and Production Forecasting | Chapter 11 | New Ideas Concerning Science and Technology Vol. 12

 Petroleum as a natural resource is depleting year after year, necessitating effective management of the resource and its reservoir. Reservoir modelling and production forecasting are critical inputs in this scenario for effective management. The spatial distribution of reservoir properties describing the reservoir and associated output profiles is difficult to estimate since naturally occurring reservoirs are extremely heterogeneous and nonlinear in nature. In terms of reservoir properties and history matching, an accurate model constructed with the aid of data obtained from the reservoir, can lead to more effective reservoir management, and such models can be created using mathematical modelling and optimization techniques. This chapter covers a variety of optimization strategies that can be used for output forecasting and background matching. Simulated Annealing (SA), Scatter Search (SS), Neighborhood algorithm (NA), Particle Swarm Optimization (PSO), and Ant Colony Optimization are examples of gradient-based and non-gradient-based optimization techniques (ACO), The application of Ensemble Kalman Filters (EnKF) and Genetic Algorithms (GA) to reservoir output history matching and efficiency. The chapter also goes into recent developments and adaptations of these techniques.

Author (s) Details

Giridhar Vadicharla
Department of Chemical Engineering, University of Petroleum and Energy Studies, Dehradun, India

Pushpa Sharma
Department of Petroleum Engineering and Earth Sciences, University of Petroleum and Energy Studies, Dehradun, India

View Book :- https://stm.bookpi.org/NICST-V12/article/view/782