ANN-Based Instantaneous Simulation of Particle Trajectories in Microfluidics

Zhang, Naiyin; Liang, Kaicong; Liu, Zhenya; Sun, Tongwen; Wang, Junchao

doi:10.3390/mi13122100

Cited by 8 publications

(3 citation statements)

References 34 publications

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“…Shchanikov et al used an ANN to design a bidirectional biointerface with nanoelectronics and microfluidics [38]. Contemporary numerical simulation software can readily integrate with machine learning techniques to iterate microdevice designs, accelerating device development and validation, e.g., the ones used for particle trajectory prediction can be adapted for predicting red blood cell (RBC) movements in microchannels [39][40][41].…”

Section: Computer-aided Microsystem Design and Optimizationmentioning

confidence: 99%

Microsystem Advances through Integration with Artificial Intelligence

2023

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Microfluidics is a rapidly growing discipline that involves studying and manipulating fluids at reduced length scale and volume, typically on the scale of micro- or nanoliters. Under the reduced length scale and larger surface-to-volume ratio, advantages of low reagent consumption, faster reaction kinetics, and more compact systems are evident in microfluidics. However, miniaturization of microfluidic chips and systems introduces challenges of stricter tolerances in designing and controlling them for interdisciplinary applications. Recent advances in artificial intelligence (AI) have brought innovation to microfluidics from design, simulation, automation, and optimization to bioanalysis and data analytics. In microfluidics, the Navier–Stokes equations, which are partial differential equations describing viscous fluid motion that in complete form are known to not have a general analytical solution, can be simplified and have fair performance through numerical approximation due to low inertia and laminar flow. Approximation using neural networks trained by rules of physical knowledge introduces a new possibility to predict the physicochemical nature. The combination of microfluidics and automation can produce large amounts of data, where features and patterns that are difficult to discern by a human can be extracted by machine learning. Therefore, integration with AI introduces the potential to revolutionize the microfluidic workflow by enabling the precision control and automation of data analysis. Deployment of smart microfluidics may be tremendously beneficial in various applications in the future, including high-throughput drug discovery, rapid point-of-care-testing (POCT), and personalized medicine. In this review, we summarize key microfluidic advances integrated with AI and discuss the outlook and possibilities of combining AI and microfluidics.

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Section: Computer-aided Microsystem Design and Optimizationmentioning

confidence: 99%

Microsystem Advances through Integration with Artificial Intelligence

2023

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“…In recent years, AI, as a modern discipline, has been widely used in performance prediction [ 37 , 38 , 39 , 40 ], floor planning [ 22 , 41 , 42 ], collaborative optimization [ 43 , 44 , 45 ], image recognition [ 46 , 47 , 48 , 49 ], defect detection [ 50 , 51 , 52 , 53 ], micromanufacturing processes [ 54 , 55 ] and other aspects of research, and has been successfully applied in microsystem SI design. The application of artificial intelligence methods to microsystem design is commonly divided into four steps [ 56 ]: (1) clarify the problem to be solved, determine the design parameters and performance parameters; (2) obtain data; (3) establish a neural networks model and use the acquired data to train neural networks to achieve performance prediction; and (4) optimize the performance through an intelligent optimization algorithm.…”

Section: Introductionmentioning

confidence: 99%

Application and Prospect of Artificial Intelligence Methods in Signal Integrity Prediction and Optimization of Microsystems

Shan

Wang

et al. 2023

Micromachines

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Microsystems are widely used in 5G, the Internet of Things, smart electronic devices and other fields, and signal integrity (SI) determines their performance. Establishing accurate and fast predictive models and intelligent optimization models for SI in microsystems is extremely essential. Recently, neural networks (NNs) and heuristic optimization algorithms have been widely used to predict the SI performance of microsystems. This paper systematically summarizes the neural network methods applied in the prediction of microsystem SI performance, including artificial neural network (ANN), deep neural network (DNN), recurrent neural network (RNN), convolutional neural network (CNN), etc., as well as intelligent algorithms applied in the optimization of microsystem SI, including genetic algorithm (GA), differential evolution (DE), deep partition tree Bayesian optimization (DPTBO), two stage Bayesian optimization (TSBO), etc., and compares and discusses the characteristics and application fields of the current applied methods. The future development prospects are also predicted. Finally, the article is summarized.

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“…4,22 These NPs were either embedded into array structures such as silicon nanowire arrays, gold pyramids, polymeric nanoneedles, etc., or employed directly. 23 So far, gold NP (AuNP)sensitised photoporation has been studied extensively (Table S2 †). 4,[24][25][26][27] The majority of the studies used spherical AuNPs of size varying from 30-250 nm with a plasmonic peak located at ∼530 nm for intracellular delivery, and laser exposure was performed at varying wavelengths: 532 nm, 28,29 800 nm (ref.…”

Section: Introductionmentioning

confidence: 99%