Effective equations in complex systems: from Langevin to machine learning

Vulpiani, Angelo; Baldovin, Marco

doi:10.1088/1742-5468/ab535c

Cited by 7 publications

(2 citation statements)

References 40 publications

(75 reference statements)

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“…They also emphasize the important role played by the high dimension of systems with a high enough number of degrees of freedom versus the intrinsic role of chaos as a limiting factor to predictability in low dimensional systems. All this is nicely described in great detail in [39] and in other recent and enlightening papers by Angelo Vulpiani and collaborators [40][41][42].…”

Section: Machine Learning and Scientific Methodsmentioning

confidence: 71%

Artificial Intelligence, Chaos, Prediction and Understanding in Science

Sanjuan

2020

Preprint

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Machine learning and deep learning techniques are contributing much to the advancement of science. Their powerful predictive capabilities appear in numerous disciplines, including chaotic dynamics, but they miss understanding. The main thesis here is that prediction and understanding are two very different and important ideas that should guide us about the progress of science. Furthermore, it is emphasized the important role played by nonlinear dynamical systems for the process of understanding. The path of the future of science will be marked by a constructive dialogue between big data and big theory, without which we cannot understand.

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Section: Machine Learning and Scientific Methodsmentioning

confidence: 71%

Artificial Intelligence, Chaos, Prediction and Understanding in Science

Sanjuan

2020

Preprint

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“…Fitting a parabolic curve of the form shown in equation (2) to MSD data in figure 4(A) produces values for mean variance per unit time and velocity, which are shown in table 1. The motion predicted by equation ( 2) can be simulated using a Langevin dynamic model of the form [49], where r is the distance relative to the floater's initial starting point and ∆ is a random number drawn from a Gaussian distribution. supplementary information contains the code used for this simulation.…”

Section: Resultsmentioning

confidence: 99%

3D printed self-propelled composite floaters

Shabaniverki¹,

Alvarez-Valdivia²,

Juárez³

2021

Smart Mater. Struct.

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This article presents a method for fabricating millimeter scale self-propelled floaters that move under their own power in random trajectories. The floaters are fabricated using fused deposition modeling of ABS scaffolds that are encapsulated in, and subsequently dissolved from, polydimethylsiloxane. The evacuated millifluidic channels left by dissolving acrylonitrile butadiene styrene (ABS) scaffolds are filled in with an ethanol-infused polyethylene glycol diacrylate hydrogel that serves as the fuel to drive propulsion in a fluid. We examine the motion of four different shapes, finding that shapes with two open ends exhibited pulsation in their trajectory, while shapes with a single open end featured trajectories that directed it to move in circles. The mean square displacement (MSD) was constructed from these trajectories to measure the mean position variance and average velocity. The floater design with a single open end was measured to have a higher mean variance per unit time (2.9 mm2 s−1) and average velocity (4.4 mm s−1). These parameters were nearly twice as high in comparison to the slowest floater design, which had an mean variance per unit time and average velocity of 1.7 mm2 s−1 and 1.5 mm s−1, respectively. In order to show that the motion behaved in a manner that is similar to Brownian motion, we simulated the trajectories using a Langevin dynamic simulation. The result of these simulations showed excellent agreement between the measured and simulation MSD. To show the utility of these structures for mixing applications, we designed a floating spinner that completely mixes a mixture of dye and water within 12 s. Ultimately, the design process illustrated here may find use in variety of platforms that require sample mixing, cargo transport and sensing.

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Ultrasound-enhanced diffusion and streaming of colloids in porous media

Yeh

Juárez

2021

Experimental Thermal and Fluid Science

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Effective equations in complex systems: from Langevin to machine learning

Cited by 7 publications

References 40 publications

Artificial Intelligence, Chaos, Prediction and Understanding in Science

Artificial Intelligence, Chaos, Prediction and Understanding in Science

3D printed self-propelled composite floaters

Ultrasound-enhanced diffusion and streaming of colloids in porous media

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