Reactor Optimization Benchmark by Reinforcement Learning
- URL: http://arxiv.org/abs/2403.14273v1
- Date: Thu, 21 Mar 2024 10:26:47 GMT
- Title: Reactor Optimization Benchmark by Reinforcement Learning
- Authors: Deborah Schwarcz, Nadav Schneider, Gal Oren, Uri Steinitz,
- Abstract summary: This paper introduces a novel benchmark problem within the OpenNeoMC framework designed specifically for reinforcement learning.
The test case features distinct local optima, representing different physical regimes, thus posing a challenge for learning algorithms.
We demonstrate the effectiveness of reinforcement learning in navigating complex optimization landscapes with strict constraints.
- Score: 0.24374097382908472
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Neutronic calculations for reactors are a daunting task when using Monte Carlo (MC) methods. As high-performance computing has advanced, the simulation of a reactor is nowadays more readily done, but design and optimization with multiple parameters is still a computational challenge. MC transport simulations, coupled with machine learning techniques, offer promising avenues for enhancing the efficiency and effectiveness of nuclear reactor optimization. This paper introduces a novel benchmark problem within the OpenNeoMC framework designed specifically for reinforcement learning. The benchmark involves optimizing a unit cell of a research reactor with two varying parameters (fuel density and water spacing) to maximize neutron flux while maintaining reactor criticality. The test case features distinct local optima, representing different physical regimes, thus posing a challenge for learning algorithms. Through extensive simulations utilizing evolutionary and neuroevolutionary algorithms, we demonstrate the effectiveness of reinforcement learning in navigating complex optimization landscapes with strict constraints. Furthermore, we propose acceleration techniques within the OpenNeoMC framework, including model updating and cross-section usage by RAM utilization, to expedite simulation times. Our findings emphasize the importance of machine learning integration in reactor optimization and contribute to advancing methodologies for addressing intricate optimization challenges in nuclear engineering. The sources of this work are available at our GitHub repository: https://github.com/Scientific-Computing-Lab-NRCN/RLOpenNeoMC
Related papers
- A Reinforcement Learning Environment for Automatic Code Optimization in the MLIR Compiler [0.10923877073891444]
We introduce the first RL environment for the MLIR compiler, dedicated to facilitating MLIR compiler research.
We also propose a novel formulation of the action space as a product of simpler action subspaces, enabling more efficient and effective optimizations.
arXiv Detail & Related papers (2024-09-17T10:49:45Z) - Center-Sensitive Kernel Optimization for Efficient On-Device Incremental Learning [88.78080749909665]
Current on-device training methods just focus on efficient training without considering the catastrophic forgetting.
This paper proposes a simple but effective edge-friendly incremental learning framework.
Our method achieves average accuracy boost of 38.08% with even less memory and approximate computation.
arXiv Detail & Related papers (2024-06-13T05:49:29Z) - Machine Learning Insides OptVerse AI Solver: Design Principles and
Applications [74.67495900436728]
We present a comprehensive study on the integration of machine learning (ML) techniques into Huawei Cloud's OptVerse AI solver.
We showcase our methods for generating complex SAT and MILP instances utilizing generative models that mirror multifaceted structures of real-world problem.
We detail the incorporation of state-of-the-art parameter tuning algorithms which markedly elevate solver performance.
arXiv Detail & Related papers (2024-01-11T15:02:15Z) - Gradual Optimization Learning for Conformational Energy Minimization [69.36925478047682]
Gradual Optimization Learning Framework (GOLF) for energy minimization with neural networks significantly reduces the required additional data.
Our results demonstrate that the neural network trained with GOLF performs on par with the oracle on a benchmark of diverse drug-like molecules.
arXiv Detail & Related papers (2023-11-05T11:48:08Z) - Machine Learning methods for simulating particle response in the Zero
Degree Calorimeter at the ALICE experiment, CERN [8.980453507536017]
Currently, over half of the computing power at CERN GRID is used to run High Energy Physics simulations.
The recent updates at the Large Hadron Collider (LHC) create the need for developing more efficient simulation methods.
We propose an alternative approach to the problem that leverages machine learning.
arXiv Detail & Related papers (2023-06-23T16:45:46Z) - A Hybrid Data-Driven Multi-Stage Deep Learning Framework for Enhanced Nuclear Reactor Power Prediction [0.4166512373146748]
This paper introduces a novel multi-stage deep learning framework for predicting the final steady-state power of reactor transients.
We use feed-forward neural networks with both classification and regression stages, and training on a unique dataset that integrates real-world measurements of reactor power and controls state.
The incorporation of simulated data with noise significantly improves the model's generalization capabilities, mitigating the risk of overfitting.
arXiv Detail & Related papers (2022-11-23T17:32:52Z) - Deep Gaussian Process-based Multi-fidelity Bayesian Optimization for
Simulated Chemical Reactors [0.0]
We apply deep Gaussian processes (DGPs) to model multi-fidelity coiled-tube reactor simulations.
The search space of reactor geometries is explored through an amalgam of different fidelity simulations.
The accuracy of simulations is determined against experimental data obtained from a 3D printed reactor configuration.
arXiv Detail & Related papers (2022-10-31T10:52:16Z) - Online hyperparameter optimization by real-time recurrent learning [57.01871583756586]
Our framework takes advantage of the analogy between hyperparameter optimization and parameter learning in neural networks (RNNs)
It adapts a well-studied family of online learning algorithms for RNNs to tune hyperparameters and network parameters simultaneously.
This procedure yields systematically better generalization performance compared to standard methods, at a fraction of wallclock time.
arXiv Detail & Related papers (2021-02-15T19:36:18Z) - Application of an automated machine learning-genetic algorithm
(AutoML-GA) coupled with computational fluid dynamics simulations for rapid
engine design optimization [0.0]
The present work describes and validates an automated active learning approach, AutoML-GA, for surrogate-based optimization of internal combustion engines.
A genetic algorithm is employed to locate the design optimum on the machine learning surrogate surface.
It is demonstrated that AutoML-GA leads to a better optimum with a lower number of CFD simulations.
arXiv Detail & Related papers (2021-01-07T17:50:52Z) - Global Optimization of Gaussian processes [52.77024349608834]
We propose a reduced-space formulation with trained Gaussian processes trained on few data points.
The approach also leads to significantly smaller and computationally cheaper sub solver for lower bounding.
In total, we reduce time convergence by orders of orders of the proposed method.
arXiv Detail & Related papers (2020-05-21T20:59:11Z) - Self-Directed Online Machine Learning for Topology Optimization [58.920693413667216]
Self-directed Online Learning Optimization integrates Deep Neural Network (DNN) with Finite Element Method (FEM) calculations.
Our algorithm was tested by four types of problems including compliance minimization, fluid-structure optimization, heat transfer enhancement and truss optimization.
It reduced the computational time by 2 5 orders of magnitude compared with directly using methods, and outperformed all state-of-the-art algorithms tested in our experiments.
arXiv Detail & Related papers (2020-02-04T20:00:28Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.