Machine intelligence in dynamical systems: \A state‐of‐art review

Sahoo, Arup Kumar; Chakraverty, Snehashish

doi:10.1002/widm.1461

Cited by 15 publications

(4 citation statements)

References 118 publications

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“…The accuracy of the proposed MhWNN-LBFGS algorithm has been shown in the tables and graphs. Different global statistical measures (NSE, MAPE, TIC, and RMSE) are evaluated, for convergence analysis of MhWNN-LBFGS model which is defined as below [30] NSE= ( )…”

Section: Simulation Results and Discussionmentioning

confidence: 99%

Modeling of Mexican Hat Wavelet Neural Network with L-BFGS Algorithm for Simulating the Recycling Procedure of Waste Plastic in Ocean

Sahoo¹,

Chakraverty²

2023

JEMSE

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In the global economy, plastics are considered a versatile and ubiquitous material. It can reach to marine ecosystems through diverse channels, such as road runoff, wastewater pathways, and improper waste management. Therefore, rapid mitigation and reduction are required for this ever-growing problem. The marine habitats are believed to be the highest emitters and absorbers of O2 and CO2 respectively. As such, every day, the prominence of managing the litter in the ocean is growing effectively and efficiently. One of the most significant challenges in oceanography is creating a comprehensive meshless algorithm to handle the mathematical representation of waste plastic management in the ocean. This research dedicates to studying the dynamics of waste plastic management model governed by a mathematical representation depending on three components viz. Waste plastic (W), Marine litter (M) and Recycling of debris (R), i.e., WMR model. In this regard, an unsupervised machine learning approach, namely Mexican Hat Wavelet Neural Network (MhWNN) refined by the efficient Limited-memory Broyden-Fletcher-Goldfarb-Shanno algorithm (L-BFGS), i.e., MhWNN-LBFGS model has been implemented for handling the non-linear phenomena of WMR models. Besides, the obtained solution is meshfree and compared with the state-of-art numerical result to establish the precision of the MhWNN-LBFGS model. Furthermore, different global statistical measures (MAPE, TIC, RMSE, and ENSE) have been computed at twenty testing points to validate the stability of the proposed algorithm.

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Section: Simulation Results and Discussionmentioning

confidence: 99%

Modeling of Mexican Hat Wavelet Neural Network with L-BFGS Algorithm for Simulating the Recycling Procedure of Waste Plastic in Ocean

Sahoo¹,

Chakraverty²

2023

JEMSE

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“…After that, researchers have extensively investigated DEs including non-linear systems, in the context of ANN. However, researchers have proposed variants of NN for solving DEs with distnict properties such as fractional DEs, fuzzy DEs, stiff DEs, disease models etc [6][7][8]. Many essential works on neural network models in order to solve DEs have been done in past few years [9][10][11][12][13].…”

Section: Related Studiesmentioning

confidence: 99%

Application of Physics-Informed Neural Networks for Simulating Mass-Spring-Damper Systems

Sahoo,

Kumar,

Chakraverty

2024

Preprint

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Dynamical problems are generally governed by a set of linear/non-linear differential equations (DEs). A large amount of prior physical information in the form of DEs plays an important role in simulation of dynamical systems. However, the traditional data-hungry machine learning models fail to express insightful scientific information from the data. Most of the implementations of neural networks are to perform non-linear mapping from input space to target space. However, Physics-informed neural networks (PINNs) can bridge the gap between scientific computing and data-hungry models. This paper exploits a new application of PINNs for approximating the behaviour of mass-spring-damper systems, showcasing how PINNs can effectively blend scientific principles with data-driven modeling. In this regard, we present solutions of two realistic application problems using PINNs. The accuracy of the predicted displacements of objects is established through results from literatures.

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“…The uncontroverted gains to health technology should not be patronized by feigned unethical organizations and individuals. A study by [9] indicated that "different types of MI and a variety of artificial neural network (ANN) methods among any related learning algorithm applications can be an incentive to a new area of research involving dynamical systems and will help in developing best performing models". In his study Bhattacharyya et al (2022) brought forward for "consideration a novel approach for covid-19 X-ray CT image categorization with the aim of coming up with an efficient tool based on the conditional generative adversarial network (C-GAN).…”

Section: Opportunity In Healthcarementioning

confidence: 99%

Efforts to a Belief and Decolonize Global Health

Matagi

2023

JAMMR

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The truth is always one: no matter the interpretations, versions, perceptions or perspectives. Supported by the majority of scientific or historical evidence the science of medicine existed an eon back and was trusted to heal and revert illnesses in addition to saving lives. The amount of finances consumed in the business aside, so much time has been and is being invested in trying to come up with better and improved diagnostic and therapeutic advances globally. Surprisingly even the rich do not trust conventional medicine a great deal, the bourgeois have the majority of their population believing in the concept and this was attributed to the fact that the majority of the bourgeois work so hard and some even imposter the rich. For better healthcare services, LMICs need to embrace and implement their data driven decisions and be the efficient leaders and managers in their own countries as they know and have an insight of their countries best. Evaluations, audits and case studies can be done by external expertise to lend hand in the strategies towards achieving the objective.

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Machine intelligence in dynamical systems: \A state‐of‐art review

Cited by 15 publications

References 118 publications

Modeling of Mexican Hat Wavelet Neural Network with L-BFGS Algorithm for Simulating the Recycling Procedure of Waste Plastic in Ocean

Modeling of Mexican Hat Wavelet Neural Network with L-BFGS Algorithm for Simulating the Recycling Procedure of Waste Plastic in Ocean

Application of Physics-Informed Neural Networks for Simulating Mass-Spring-Damper Systems

Efforts to a Belief and Decolonize Global Health

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