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Optimization Methods for SIMLIB/C++ Simulation Library
Chlebík, Jakub ; Janoušek, Vladimír (referee) ; Peringer, Petr (advisor)
This thesis addresses the topic of parametric optimization of simulation models. It introduces theoretical foundation of optimization and its uses in simulation analysis. Furthermore, it suggests the extension of SIMBLI/C++ library by module for optimization methods. Some of the chosen methods are then theoretically described, implemented in C++ language, demonstrates its uses and evaluates their success.
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Acceleration of Neurostimulation Using Artificial Intelligence Methods
Gaňo, Martin ; Chlebík, Jakub (referee) ; Jaroš, Jiří (advisor)
Treatment using transcranial ultrasound is a rapidly arising domain of medicine. This method brings options for non-invasive brain therapies, including ablation, neuromodulation, or potentially opening the blood-brain barrier for the following treatment. The health officer needs to constantly receive feedback on the ultrasound wavefield in the human skull in real-time to accomplish the cure using these techniques. The traditional methods for simulating monochromous ultrasound waves are computationally too expensive. That is why their usage would be infeasible for these purposes, and it brings the need for alternative methods. This work proposed and implemented a method to solve the Helmholtz equation in 3D space using a neural network achieving a faster convergence rate. The neural network design uses lightweight architecture based on UNet. The main interest of this work is neuromodulation because, in this application, it is possible to ignore several variables and phenomena that would not be negligible in other use cases. Omitting them from the calculations increased the chances of accomplishing computations in a reasonable time. The method is fully unsupervised and uses exclusively artificially generated spherical harmonics and physics-based loss for training, with no required ground truth labels. Results showed a faster calculation with acceptable error than other traditional methods.
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Estimation of Algorithm Execution Time Using Machine Learning
Buchta, Martin ; Chlebík, Jakub (referee) ; Jaroš, Jiří (advisor)
This work aims to predict the execution time of k-Wave ultrasound simulations on supercomputers based on a given domain size. The program uses MPI and can be run on multiple nodes. Prediction models were developed using symbolic regression and neural networks, both of which trained on captured data and compared against each other. The results demonstrate that the models outperform existing solutions. Specifically, the symbolic regression model achieved an average error of 5.64% for suitable tasks, while the neural network model achieved an average error of 8.25% on unseen domain sizes and across all tasks, including those not optimized for k-Wave simulations. This work contributes a new, more accurate model for predicting execution time, and compares the effectiveness of neural networks and symbolic regression for this specific type of regression problem. Overall, these findings suggest that new models will have important practical applications in the field of k-Wave ultrasound simulations.
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Acceleration of Neurostimulation Using Artificial Intelligence Methods
Gaňo, Martin ; Chlebík, Jakub (referee) ; Jaroš, Jiří (advisor)
Treatment using transcranial ultrasound is a rapidly arising domain of medicine. This method brings options for non-invasive brain therapies, including ablation, neuromodulation, or potentially opening the blood-brain barrier for the following treatment. The health officer needs to constantly receive feedback on the ultrasound wavefield in the human skull in real-time to accomplish the cure using these techniques. The traditional methods for simulating monochromous ultrasound waves are computationally too expensive. That is why their usage would be infeasible for these purposes, and it brings the need for alternative methods. This work proposed and implemented a method to solve the Helmholtz equation in 3D space using a neural network achieving a faster convergence rate. The neural network design uses lightweight architecture based on UNet. The main interest of this work is neuromodulation because, in this application, it is possible to ignore several variables and phenomena that would not be negligible in other use cases. Omitting them from the calculations increased the chances of accomplishing computations in a reasonable time. The method is fully unsupervised and uses exclusively artificially generated spherical harmonics and physics-based loss for training, with no required ground truth labels. Results showed a faster calculation with acceptable error than other traditional methods.
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Optimization Methods for SIMLIB/C++ Simulation Library
Chlebík, Jakub ; Janoušek, Vladimír (referee) ; Peringer, Petr (advisor)
This thesis addresses the topic of parametric optimization of simulation models. It introduces theoretical foundation of optimization and its uses in simulation analysis. Furthermore, it suggests the extension of SIMBLI/C++ library by module for optimization methods. Some of the chosen methods are then theoretically described, implemented in C++ language, demonstrates its uses and evaluates their success.
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