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Register a new userPreliminary results in using Deep Learning to emulate BLOB, a nuclear interaction model
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Purpose: A reliable model to simulate nuclear interactions is fundamental for Ion-therapy. We already showed how BLOB (Boltzmann-Langevin One Body), a model developed to simulate heavy ion interactions up to few hundreds of MeV/u, could simulate also $^{12}$C reactions in the same energy domain. However, its computation time is too long for any medical application. For this reason we present the possibility of emulating it with a Deep Learning algorithm. Methods: The BLOB final state is a Probability Density Function (PDF) of finding a nucleon in a position of the phase space. We discretised this PDF and trained a Variational Auto-Encoder (VAE) to reproduce such a discrete PDF. As a proof of concept, we developed and trained a VAE to emulate BLOB in simulating the interactions of $^{12}$C with $^{12}$C at 62 MeV/u. To have more control on the generation, we forced the VAE latent space to be organised with respect to the impact parameter ($b$) training a classifier of $b$ jointly with the VAE. Results: The distributions obtained from the VAE are similar to the input ones and the computation time needed to use the VAE as a generator is negligible. Conclusions: We show that it is possible to use a Deep Learning approach to emulate a model developed to simulate nuclear reactions in the energy range of interest for Ion-therapy. We foresee the implementation of the generation part in C++ and to interface it with the most used Monte Carlo toolkit: Geant4.
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Energy loss of energetic ions in solid is crucial in many field, and accurate prediction of the ion stopping power is a long-time goal. Though great efforts have been made, it is still very difficult to find a universal prediction model to accurately calculate the ion stopping power in distinct target materials. Deep learning algorithm is a newly emerged method to solve multi-factors physical problems and can mine the deeply implicit relations among parameters, which make it a powerful tool in energy loss prediction. In this work, we developed an energy loss prediction model based on deep learning. When experimental data are available, our model can give predictions with an average absolute difference close to 5.7%, which is in the same level compared with other widely used programs e.g. SRIM. In the regime without experimental data, our model still can maintain a high performance, and has higher reliability compared with the existing models. The ion range of Au ions in SiC can be calculated with a relative error of 0.6~25% for ions in the energy range of 700~10000 keV, which is much better than the results calculated by SRIM. Moreover, our model support the reciprocity conjecture of ion stopping power in solid proposed by P. Sigmund, which has been known for a long time but can hardly been proved by any of the existing stopping power models. This high-accuracy energy loss prediction model is very important for the research of ion-solid interaction mechanism and enormous relevant applications of energetic ions, such as in semiconductor fabrications, nuclear energy systems and the space facilities.
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Reduced Order Modeling (ROM) for engineering applications has been a major research focus in the past few decades due to the unprecedented physical insight into turbulence offered by high-fidelity CFD. The primary goal of a ROM is to model the key physics/features of a flow-field without computing the full Navier-Stokes (NS) equations. This is accomplished by projecting the high-dimensional dynamics to a low-dimensional subspace, typically utilizing dimensionality reduction techniques like Proper Orthogonal Decomposition (POD), coupled with Galerkin projection. In this work, we demonstrate a deep learning based approach to build a ROM using the POD basis of canonical DNS datasets, for turbulent flow control applications. We find that a type of Recurrent Neural Network, the Long Short Term Memory (LSTM) which has been primarily utilized for problems like speech modeling and language translation, shows attractive potential in modeling temporal dynamics of turbulence. Additionally, we introduce the Hurst Exponent as a tool to study LSTM behavior for non-stationary data, and uncover useful characteristics that may aid ROM development for a variety of applications.
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