Exploring the potential of microfluidics for next-generation drug delivery systems

Bhattacharjee, Gargi; Gohil, Nisarg; Shukla, Malvika; Sharma, Swati; Mani, Indra; Pandya, Alok; Chu, Dinh‐Toi; Bui, Nhat Le; Thi, Yen-Vy Nguyen; Khambhati, Khushal; Maurya, Rupesh; Ramakrishna, Suresh; Singh, Vijai

doi:10.1016/j.onano.2023.100150

Cited by 7 publications

(1 citation statement)

References 121 publications

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“…A great number of reviews meant that an even greater number of original research articles had been published. If we limited the results of the Scopus search to reviews related to fluids, three major fields of application emerge that attracted research interest in applying ML methods to (a) theoretical and industrial oil and gas [33][34][35][36][37][38][39][40][41][42][43][44][45][46][47][48][49][50][51][52], (b) CFD simulations [53][54][55][56][57][58][59][60][61][62][63][64][65][66][67][68], and (c) health and medical applications [69][70][71][72][73][74][75][76][77][78][79][80]…”

Section: A Brief Overview and Methods Classificationmentioning

confidence: 99%

Can Artificial Intelligence Accelerate Fluid Mechanics Research?

Drikakis

Sofos

2023

Fluids

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The significant growth of artificial intelligence (AI) methods in machine learning (ML) and deep learning (DL) has opened opportunities for fluid dynamics and its applications in science, engineering and medicine. Developing AI methods for fluid dynamics encompass different challenges than applications with massive data, such as the Internet of Things. For many scientific, engineering and biomedical problems, the data are not massive, which poses limitations and algorithmic challenges. This paper reviews ML and DL research for fluid dynamics, presents algorithmic challenges and discusses potential future directions.

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