Application of an automated machine learning-genetic algorithm
(AutoML-GA) coupled with computational fluid dynamics simulations for rapid
engine design optimization
- URL: http://arxiv.org/abs/2101.02653v3
- Date: Tue, 6 Apr 2021 15:10:23 GMT
- Title: Application of an automated machine learning-genetic algorithm
(AutoML-GA) coupled with computational fluid dynamics simulations for rapid
engine design optimization
- Authors: Opeoluwa Owoyele, Pinaki Pal, Alvaro Vidal Torreira, Daniel Probst,
Matthew Shaxted, Michael Wilde, Peter Kelly Senecal
- Abstract summary: 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.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: In recent years, the use of machine learning-based surrogate models for
computational fluid dynamics (CFD) simulations has emerged as a promising
technique for reducing the computational cost associated with engine design
optimization. However, such methods still suffer from drawbacks. One main
disadvantage of is that the default machine learning (ML) hyperparameters are
often severely suboptimal for a given problem. This has often been addressed by
manually trying out different hyperparameter settings, but this solution is
ineffective in a high-dimensional hyperparameter space. Besides this problem,
the amount of data needed for training is also not known a priori. In response
to these issues that need to be addressed, the present work describes and
validates an automated active learning approach, AutoML-GA, for surrogate-based
optimization of internal combustion engines. In this approach, a Bayesian
optimization technique is used to find the best machine learning
hyperparameters based on an initial dataset obtained from a small number of CFD
simulations. Subsequently, a genetic algorithm is employed to locate the design
optimum on the ML surrogate surface. In the vicinity of the design optimum, the
solution is refined by repeatedly running CFD simulations at the projected
optimum and adding the newly obtained data to the training dataset. It is
demonstrated that AutoML-GA leads to a better optimum with a lower number of
CFD simulations, compared to the use of default hyperparameters. The proposed
framework offers the advantage of being a more hands-off approach that can be
readily utilized by researchers and engineers in industry who do not have
extensive machine learning expertise.
Related papers
- Machine Learning Optimized Approach for Parameter Selection in MESHFREE Simulations [0.0]
Meshfree simulation methods are emerging as compelling alternatives to conventional mesh-based approaches.
We provide a comprehensive overview of our research combining Machine Learning (ML) and Fraunhofer's MESHFREE software.
We introduce a novel ML-optimized approach, using active learning, regression trees, and visualization on MESHFREE simulation data.
arXiv Detail & Related papers (2024-03-20T15:29:59Z) - Federated Conditional Stochastic Optimization [110.513884892319]
Conditional optimization has found in a wide range of machine learning tasks, such as in-variant learning tasks, AUPRC, andAML.
This paper proposes algorithms for distributed federated learning.
arXiv Detail & Related papers (2023-10-04T01:47:37Z) - Learning Controllable Adaptive Simulation for Multi-resolution Physics [86.8993558124143]
We introduce Learning controllable Adaptive simulation for Multi-resolution Physics (LAMP) as the first full deep learning-based surrogate model.
LAMP consists of a Graph Neural Network (GNN) for learning the forward evolution, and a GNN-based actor-critic for learning the policy of spatial refinement and coarsening.
We demonstrate that our LAMP outperforms state-of-the-art deep learning surrogate models, and can adaptively trade-off computation to improve long-term prediction error.
arXiv Detail & Related papers (2023-05-01T23:20:27Z) - Sample-Efficient and Surrogate-Based Design Optimization of Underwater Vehicle Hulls [0.4543820534430522]
We show that theBO-LCB algorithm is the most sample-efficient optimization framework and has the best convergence behavior of those considered.
We also show that our DNN-based surrogate model predicts drag force on test data in tight agreement with CFD simulations, with a mean absolute percentage error (MAPE) of 1.85%.
We demonstrate a two-orders-of-magnitude speedup for the design optimization process when the surrogate model is used.
arXiv Detail & Related papers (2023-04-24T19:52:42Z) - Surrogate Neural Networks for Efficient Simulation-based Trajectory
Planning Optimization [28.292234483886947]
This paper presents a novel methodology that uses surrogate models in the form of neural networks to reduce the computation time of simulation-based optimization of a reference trajectory.
We find a 74% better-performing reference trajectory compared to nominal, and the numerical results clearly show a substantial reduction in computation time for designing future trajectories.
arXiv Detail & Related papers (2023-03-30T15:44:30Z) - An Empirical Evaluation of Zeroth-Order Optimization Methods on
AI-driven Molecule Optimization [78.36413169647408]
We study the effectiveness of various ZO optimization methods for optimizing molecular objectives.
We show the advantages of ZO sign-based gradient descent (ZO-signGD)
We demonstrate the potential effectiveness of ZO optimization methods on widely used benchmark tasks from the Guacamol suite.
arXiv Detail & Related papers (2022-10-27T01:58:10Z) - Data-driven evolutionary algorithm for oil reservoir well-placement and
control optimization [3.012067935276772]
Generalized data-driven evolutionary algorithm (GDDE) is proposed to reduce the number of simulation runs on well-placement and control optimization problems.
Probabilistic neural network (PNN) is adopted as the classifier to select informative and promising candidates.
arXiv Detail & Related papers (2022-06-07T09:07:49Z) - Conservative Objective Models for Effective Offline Model-Based
Optimization [78.19085445065845]
Computational design problems arise in a number of settings, from synthetic biology to computer architectures.
We propose a method that learns a model of the objective function that lower bounds the actual value of the ground-truth objective on out-of-distribution inputs.
COMs are simple to implement and outperform a number of existing methods on a wide range of MBO problems.
arXiv Detail & Related papers (2021-07-14T17:55:28Z) - Enhanced data efficiency using deep neural networks and Gaussian
processes for aerodynamic design optimization [0.0]
Adjoint-based optimization methods are attractive for aerodynamic shape design.
They can become prohibitively expensive when multiple optimization problems are being solved.
We propose a machine learning enabled, surrogate-based framework that replaces the expensive adjoint solver.
arXiv Detail & Related papers (2020-08-15T15:09:21Z) - Automatically Learning Compact Quality-aware Surrogates for Optimization
Problems [55.94450542785096]
Solving optimization problems with unknown parameters requires learning a predictive model to predict the values of the unknown parameters and then solving the problem using these values.
Recent work has shown that including the optimization problem as a layer in a complex training model pipeline results in predictions of iteration of unobserved decision making.
We show that we can improve solution quality by learning a low-dimensional surrogate model of a large optimization problem.
arXiv Detail & Related papers (2020-06-18T19:11:54Z) - 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.