Heat Source Layout Optimization Using Automatic Deep Learning Surrogate
and Multimodal Neighborhood Search Algorithm
- URL: http://arxiv.org/abs/2205.07812v2
- Date: Mon, 4 Jul 2022 07:15:36 GMT
- Title: Heat Source Layout Optimization Using Automatic Deep Learning Surrogate
and Multimodal Neighborhood Search Algorithm
- Authors: Jialiang Sun and Xiaohu Zheng and Wen Yao and Xiaoya Zhang and Weien
Zhou and Xiaoqian Chen
- Abstract summary: In satellite layout design, heat source layout optimization (HSLO) is an effective technique to decrease the maximum temperature and improve the heat management of the whole system.
Recently, deep learning surrogate assisted HSLO has been proposed, which learns the mapping from layout to its corresponding temperature field.
- Score: 2.596774017390572
- License: http://creativecommons.org/publicdomain/zero/1.0/
- Abstract: In satellite layout design, heat source layout optimization (HSLO) is an
effective technique to decrease the maximum temperature and improve the heat
management of the whole system. Recently, deep learning surrogate assisted HSLO
has been proposed, which learns the mapping from layout to its corresponding
temperature field, so as to substitute the simulation during optimization to
decrease the computational cost largely. However, it faces two main challenges:
1) the neural network surrogate for the certain task is often manually designed
to be complex and requires rich debugging experience, which is challenging for
the designers in the engineering field; 2) existing algorithms for HSLO could
only obtain a near optimal solution in single optimization and are easily
trapped in local optimum. To address the first challenge, considering reducing
the total parameter numbers and ensuring the similar accuracy as well as, a
neural architecture search (NAS) method combined with Feature Pyramid Network
(FPN) framework is developed to realize the purpose of automatically searching
for a small deep learning surrogate model for HSLO. To address the second
challenge, a multimodal neighborhood search based layout optimization algorithm
(MNSLO) is proposed, which could obtain more and better approximate optimal
design schemes simultaneously in single optimization. Finally, two typical
two-dimensional heat conduction optimization problems are utilized to
demonstrate the effectiveness of the proposed method. With the similar
accuracy, NAS finds models with 80% fewer parameters, 64% fewer FLOPs and 36%
faster inference time than the original FPN. Besides, with the assistance of
deep learning surrogate by automatic search, MNSLO could achieve multiple near
optimal design schemes simultaneously to provide more design diversities for
designers.
Related papers
- 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) - Energy Efficiency Maximization in IRS-Aided Cell-Free Massive MIMO
System [2.9081408997650375]
In this paper, we consider an intelligent reflecting surface (IRS)-aided cell-free massive multiple-input multiple-output system, where the beamforming at access points and the phase shifts at IRSs are jointly optimized to maximize energy efficiency (EE)
To solve EE problem, we propose an iterative optimization algorithm by using quadratic transform and Lagrangian dual transform to find the optimum beamforming and phase shifts.
We further propose a deep learning based approach for joint beamforming and phase shifts design. Specifically, a two-stage deep neural network is trained offline using the unsupervised learning manner, which is then deployed online for
arXiv Detail & Related papers (2022-12-24T14:58:15Z) - High-Speed Resource Allocation Algorithm Using a Coherent Ising Machine
for NOMA Systems [3.6406488220483326]
A key challenge to fully utilizing the effectiveness of the NOMA technique is the optimization of the resource allocation.
We propose the coherent Ising machine (CIM) based optimization method for channel allocation in NOMA systems.
We show that our proposed method is superior in terms of speed and the attained optimal solutions.
arXiv Detail & Related papers (2022-12-03T09:22:54Z) - Approaching Globally Optimal Energy Efficiency in Interference Networks
via Machine Learning [22.926877147296594]
This work presents a machine learning approach to optimize the energy efficiency (EE) in a multi-cell wireless network.
Results show that the method achieves an EE close to the optimum by the branch-and- computation testing.
arXiv Detail & Related papers (2022-11-25T08:36:34Z) - A Framework for Discovering Optimal Solutions in Photonic Inverse Design [0.0]
Photonic inverse design has emerged as an indispensable engineering tool for complex optical systems.
Finding solutions approaching global optimum may present a computationally intractable task.
We develop a framework that allows expediting the search of solutions close to global optimum on complex optimization spaces.
arXiv Detail & Related papers (2021-06-03T22:11:03Z) - Towards Optimally Efficient Tree Search with Deep Learning [76.64632985696237]
This paper investigates the classical integer least-squares problem which estimates signals integer from linear models.
The problem is NP-hard and often arises in diverse applications such as signal processing, bioinformatics, communications and machine learning.
We propose a general hyper-accelerated tree search (HATS) algorithm by employing a deep neural network to estimate the optimal estimation for the underlying simplified memory-bounded A* algorithm.
arXiv Detail & Related papers (2021-01-07T08:00:02Z) - Evolving Search Space for Neural Architecture Search [70.71153433676024]
We present a Neural Search-space Evolution (NSE) scheme that amplifies the results from the previous effort by maintaining an optimized search space subset.
We achieve 77.3% top-1 retrain accuracy on ImageNet with 333M FLOPs, which yielded a state-of-the-art performance.
When the latency constraint is adopted, our result also performs better than the previous best-performing mobile models with a 77.9% Top-1 retrain accuracy.
arXiv Detail & Related papers (2020-11-22T01:11:19Z) - Federated Learning via Intelligent Reflecting Surface [30.935389187215474]
Over-the-air computation algorithm (AirComp) based learning (FL) is capable of achieving fast model aggregation by exploiting the waveform superposition property of multiple access channels.
In this paper, we propose a two-step optimization framework to achieve fast yet reliable model aggregation for AirComp-based FL.
Simulation results will demonstrate that our proposed framework and the deployment of an IRS can achieve a lower training loss and higher FL prediction accuracy than the baseline algorithms.
arXiv Detail & Related papers (2020-11-10T11:29:57Z) - Combining Deep Learning and Optimization for Security-Constrained
Optimal Power Flow [94.24763814458686]
Security-constrained optimal power flow (SCOPF) is fundamental in power systems.
Modeling of APR within the SCOPF problem results in complex large-scale mixed-integer programs.
This paper proposes a novel approach that combines deep learning and robust optimization techniques.
arXiv Detail & Related papers (2020-07-14T12:38:21Z) - Iterative Algorithm Induced Deep-Unfolding Neural Networks: Precoding
Design for Multiuser MIMO Systems [59.804810122136345]
We propose a framework for deep-unfolding, where a general form of iterative algorithm induced deep-unfolding neural network (IAIDNN) is developed.
An efficient IAIDNN based on the structure of the classic weighted minimum mean-square error (WMMSE) iterative algorithm is developed.
We show that the proposed IAIDNN efficiently achieves the performance of the iterative WMMSE algorithm with reduced computational complexity.
arXiv Detail & Related papers (2020-06-15T02:57:57Z) - 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.