On-device research index

arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

169,051 papers · 148 categories

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4 results for “Microfluidics”

Code-division multiplexed resistive pulse sensor networks for spatio-temporal detection of particles in microfluidic devicescs.ET

Enhanced microfluidic sensing platform for particle detection.

problem Electronically acquiring spatially separated particle information in lab-on-a-chip devices.

method Microfluidic CODES combining resistive pulse sensing and code division multiple access.

result Non-orthogonal code waveforms and machine learning for improved multiplexing.

microfluidics resistive pulse sensing code division multiple access machine learning particle detection

2018-08-10abs ↗pdf ↗

Flow Shape Design for Microfluidic Devices Using Deep Reinforcement Learningcs.LG

A paper uses RL to design microfluidic flow shapes efficiently.

problem Designing complex flow shapes in microfluidics using inverse problems.

method Formulated as a Reinforcement Learning (RL) problem, trained a DoubleDQN agent.

result Success frequency reached 90% in 200,000 episodes, rewards converged.

Reinforcement Learning Inverse Problems Microfluidics Flow Sculpting DoubleDQN

2018-11-29abs ↗pdf ↗

Neural Network Solutions to Differential Equations in Non-Convex Domains: Solving the Electric Field in the Slit-Well Microfluidic Devicephysics.comp-ph

Neural nets solve electric field in non-convex microfluidic devices.

problem Solving differential equations in non-convex geometries.

method Neural network approximation of electric potential and field.

result Deep neural networks outperform shallow networks in accuracy.

neural networks differential equations electric field microfluidics non-convex domains

2020-04-25abs ↗pdf ↗

Optimal Lattice Boltzmann Closures through Multi-Agent Reinforcement Learningphysics.flu-dyn

MARL improves LBM stability and accuracy across scales.

problem Stability and accuracy issues in under-resolved LBM simulations.

method Multi-Agent Reinforcement Learning (MARL) to dynamically control local relaxation parameters.

result MARL closures stabilize simulations and recover spectra of fully resolved models.

Lattice Boltzmann Method Reinforcement Learning Hydrodynamics Stability Accuracy

2025-04-19abs ↗pdf ↗