Poster
in
Workshop: D3S3: Data-driven and Differentiable Simulations, Surrogates, and Solvers

Optimizing the IFMIF-DONES Particle Accelerator with Differentiable Deep Learning Surrogate Models

Galo Gallardo · Guillermo Rodriguez Llorente · Lucas Magariños · Rodrigo Morant Navascués · Nikita Kkhvatkin Petrovsky · Roberto Gómez-Espinosa Martín

Keywords: Gradient Descent Inverse Problem Deep Learning Surrogate Models Computational Physics Neural Network Architecture Computational Efficiency PyTorch Nuclear Fusion Inverse Problems in Physics Particle Accelerator Optimization Scientific Experiment Optimization Deep Learning Surrogate Models Parameter Optimization Beam Configuration Optimization Deuteron Beam Dynamics High-Fidelity Simulations Beam Transport Modeling Differentiable Surrogate Models Scientific Computing Fast Inference Models Neural Operators Beam Profile Prediction IFMIF-DONES Scientific Facility Enhancement Machine Learning for Nuclear Fusion Fourier Neural Operators Neutron Source Facility Physics-Informed Machine Learning OPAL Simulations Simulation Speedup Partial Differential Equations Accelerator Physics Accelerated Simulation NVIDIA Modulus Neutron Irradiation Surrogate Modeling Techniques Linear Accelerator Data-Driven Optimization Accelerator Design Quadrupole Optimization Fourier Neural Operator IFMIF-DONES accelerator High Energy Beam Transport Line Numerical Methods in Physics

Project Page [ Poster] [ OpenReview]

Abstract

In this work, Deep Learning Surrogate Models are employed to optimize the quadrupole values in the initial section of the High Energy Beam Transport Line of the IFMIF-DONES accelerator. Two Fourier Neural Operator models were trained: one for predicting two-dimensional beam profiles and another for forecasting one-dimensional beam statistics along the accelerator's longitudinal axis. These models offer up to 3 orders of magnitude speedup compared to traditional simulations, with a trade-off of maintaining accuracy within percentage errors below 6$\%$. Moreover, their differentiability allows seamless integration with optimization algorithms, enabling efficient tuning of quadrupole values to achieve specific beam objectives. This approach offers a robust solution for enhancing the performance of IFMIF-DONES accelerator and other scientific experiments.

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