PSO-PINN: Physics-Informed Neural Networks Trained with Particle Swarm Optimization

Davi, Caio; Braga-Neto, Ulisses

doi:10.48550/arxiv.2202.01943

Cited by 4 publications

(2 citation statements)

References 29 publications

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“…The particles are first placed in a random location at the start of the search process. By weighing the acceleration coefficients with random factors, it is possible to increase the effectiveness solution [24] . For acceleration, the pbest and gbest sites both provide unique random numbers [25] .…”

Section: Particle Swarm Optimizationmentioning

confidence: 99%

Enhancing Resource Allocation in Edge and Fog-Cloud Computing with Genetic Algorithm and Particle Swarm Optimization

Chafi,

Balboul,

Fattah

et al. 2023

Intell. and Converged Netw.

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Evolutionary algorithms have gained significant attention from researchers as effective solutions for various optimization problems. Genetic Algorithm (GA) is widely popular due to its logical approach, broad applicability, and ability to tackle complex issues encountered in engineering systems. However, GA is known for its high implementation cost and typically requires a large number of iterations. On the other hand, Particle Swarm Optimization (PSO) is a relatively new heuristic technique inspired by the collective behaviors of real organisms. Both GA and PSO algorithms are prominent heuristic optimization methods that belong to the population-based approaches family. While they are often seen as competitors, their efficiency heavily relies on the parameter values chosen and the specific optimization problem at hand. In this study, we aim to compare the runtime performance of GA and PSO algorithms within a cutting-edge edge and fog cloud architecture. Through extensive experiments and performance evaluations, the authors demonstrate the effectiveness of GA and PSO algorithms in improving resource allocation in edge and fog cloud computing scenarios using FogWorkflowSim simulator. The comparative analysis sheds light on the strengths and limitations of each algorithm, providing valuable insights for researchers and practitioners in the field.

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Section: Particle Swarm Optimizationmentioning

confidence: 99%

Enhancing Resource Allocation in Edge and Fog-Cloud Computing with Genetic Algorithm and Particle Swarm Optimization

Chafi,

Balboul,

Fattah

et al. 2023

Intell. and Converged Netw.

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“…A Bayesian approach to PINN is presented for forward and inverse problems [48], and the idea of physics-informed adversarial training to solve PDE is proposed [49]. Particle swarm optimization is also put forward to PIDL training [50].…”

Section: Introductionmentioning

confidence: 99%

On the Limitations of Physics-Informed Deep Learning: Illustrations Using First-Order Hyperbolic Conservation Law-Based Traffic Flow Models

Huang

Agarwal

2023

IEEE Open J. Intell. Transp. Syst.

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Since its introduction in 2017, physics-informed deep learning (PIDL) has garnered growing popularity in understanding the systems governed by physical laws in terms of partial differential equations (PDEs). However, empirical evidence points to the limitations of PIDL for learning certain types of PDEs. In this paper, we (a) present the challenges in training PIDL architecture, (b) contrast the performance of PIDL architecture in learning a first order scalar hyperbolic conservation law and its parabolic counterpart, (c) investigate the effect of training data sampling, which corresponds to various sensing scenarios in traffic networks, (d) comment on the implications of PIDL limitations for traffic flow estimation and prediction in practice. Case studies present the contrast in PIDL results between learning the traffic flow model (LWR PDE) and its diffusive variation. The outcome indicates that PIDL experiences significant challenges in learning the hyperbolic LWR equation due to the non-smoothness of its solution. Conversely, the architecture with parabolic PDE, augmented with the diffusion term, leads to the successful reassembly of the density data even with the shockwaves present. The paper concludes by providing a discussion on recent assessments of reasons behind the challenge PIDL encounters with hyperbolic PDEs and the corresponding mitigation strategies.

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Physics-informed neural network for engineers: a review from an implementation aspect

Ryu,

Park,

Lee

et al. 2024

J Mech Sci Technol

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PSO-PINN: Physics-Informed Neural Networks Trained with Particle Swarm Optimization

Cited by 4 publications

References 29 publications

Enhancing Resource Allocation in Edge and Fog-Cloud Computing with Genetic Algorithm and Particle Swarm Optimization

Enhancing Resource Allocation in Edge and Fog-Cloud Computing with Genetic Algorithm and Particle Swarm Optimization

On the Limitations of Physics-Informed Deep Learning: Illustrations Using First-Order Hyperbolic Conservation Law-Based Traffic Flow Models

Physics-informed neural network for engineers: a review from an implementation aspect

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