Optimal Navigation in Microfluidics via the Optimization of a Discrete Loss | Paper Citation Record

Observation 4a771f44-1aee-4ded-bc91-ab886c8aa94e · outbound

This paper cites Compu- tational oncology—mathematical modelling of drug regimens for precision medicine.Nature reviews Clinical oncology, 13(4):242–254, 2016.

Optimal Navigation in Microfluidics via the Optimization of a Discrete Loss Compu- tational oncology—mathematical modelling of drug regimens for precision medicine.Nature reviews Clinical oncology, 13(4):242–254, 2016

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:37:12.960625Z

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Observation bf68cef5-091b-4f10-a00e-23e2aea9be89 · outbound

This paper cites Cell manipulation in microfluidics.Biofabrication, 5(2):022001, 2013.

Optimal Navigation in Microfluidics via the Optimization of a Discrete Loss Cell manipulation in microfluidics.Biofabrication, 5(2):022001, 2013

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:37:12.942697Z

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Observation 44237dd4-c855-498d-aef9-4bc9dbc64a1e · outbound

This paper cites Micro-manipulation using rotational fluid flows induced by remote magnetic micro-manipulators.Journal of Applied Physics, 112(6), 2012.

Optimal Navigation in Microfluidics via the Optimization of a Discrete Loss Micro-manipulation using rotational fluid flows induced by remote magnetic micro-manipulators.Journal of Applied Physics, 112(6), 2012

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-15T19:37:11.572451Z

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Observation e0251630-e07f-44dd-a3c4-9f72bf725688 · outbound

This paper cites Control and transport of passive particles using self-organized spinning micro-disks.IEEE Robotics and Automation Letters, 7(2):2156–2161, 2022.

Optimal Navigation in Microfluidics via the Optimization of a Discrete Loss Control and transport of passive particles using self-organized spinning micro-disks.IEEE Robotics and Automation Letters, 7(2):2156–2161, 2022

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:37:12.914237Z

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Observation df76a57c-1d72-4a2b-a352-e0fa72feca7d · outbound

This paper cites Optimal navigation strategies for active particles.Europhysics Letters, 127(3):34003, 2019.

Optimal Navigation in Microfluidics via the Optimization of a Discrete Loss Optimal navigation strategies for active particles.Europhysics Letters, 127(3):34003, 2019

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:37:12.897310Z

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Observation 25e3819a-e4a0-4cc1-9d36-4be51cdd0209 · outbound

This paper cites Independent control and path planning of microswimmers with a uniform magnetic field.Advanced Intelligent Systems, 4(3):2100183, 2022.

Optimal Navigation in Microfluidics via the Optimization of a Discrete Loss Independent control and path planning of microswimmers with a uniform magnetic field.Advanced Intelligent Systems, 4(3):2100183, 2022

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:37:12.879894Z

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Observation ad501133-a748-48cd-837e-9894ac2f4a04 · outbound

This paper cites Recent advances in microswimmers for biomedical applications.

Optimal Navigation in Microfluidics via the Optimization of a Discrete Loss Recent advances in microswimmers for biomedical applications

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:37:12.861065Z

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Observation cd707826-fd99-4665-9cd5-36fbc6cdf5c8 · outbound

This paper cites Light-driven micro-and nanomotors for envi- ronmental remediation.Environmental Science: Nano, 4(8):1602–1616, 2017.

Optimal Navigation in Microfluidics via the Optimization of a Discrete Loss Light-driven micro-and nanomotors for envi- ronmental remediation.Environmental Science: Nano, 4(8):1602–1616, 2017

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:37:12.844083Z

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Observation 56cd1ff6-380d-4e88-a2ef-244e015df847 · outbound

This paper cites Solving inverse problems in physics by optimizing a discrete loss: Fast and accurate learning without neural networks.PNAS Nexus, page pgae005, 01 2024.

Optimal Navigation in Microfluidics via the Optimization of a Discrete Loss Solving inverse problems in physics by optimizing a discrete loss: Fast and accurate learning without neural networks.PNAS Nexus, page pgae005, 01 2024

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:37:12.826882Z

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Observation e11586c5-0b3e-421d-8658-eed7602bcf81 · outbound

This paper cites An introduction to trajectory optimization: How to do your own direct collocation.

Optimal Navigation in Microfluidics via the Optimization of a Discrete Loss An introduction to trajectory optimization: How to do your own direct collocation

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:37:12.809648Z

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Observation 0a305362-650e-42c6-a28b-7633834fc607 · outbound

This paper cites Direct collocation methods for trajectory optimization in constrained robotic systems.IEEE Transactions on Robotics, 2022.

Optimal Navigation in Microfluidics via the Optimization of a Discrete Loss Direct collocation methods for trajectory optimization in constrained robotic systems.IEEE Transactions on Robotics, 2022

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:37:12.792250Z

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Observation 4230c36b-5eee-46a4-b877-4f48c25d2164 · outbound

This paper cites SIAM, 2010.

Optimal Navigation in Microfluidics via the Optimization of a Discrete Loss SIAM, 2010

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-15T19:37:11.622594Z

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Observation 5e8e0728-7356-4c49-ac51-6d4903cb0049 · outbound

This paper cites Back-propagation neural networks for nonlinear self-tuning adaptive control.IEEE control systems Magazine, 10(3):44–48, 1990.

Optimal Navigation in Microfluidics via the Optimization of a Discrete Loss Back-propagation neural networks for nonlinear self-tuning adaptive control.IEEE control systems Magazine, 10(3):44–48, 1990

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:37:12.762744Z

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Observation 4ee140d1-7922-4773-906b-a1e038ecd787 · outbound

This paper cites Neural networks for control sys- tems—a survey.Automatica, 28(6):1083–1112, 1992.

Optimal Navigation in Microfluidics via the Optimization of a Discrete Loss Neural networks for control sys- tems—a survey.Automatica, 28(6):1083–1112, 1992

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:37:12.745404Z

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Observation 4a2e5d56-f8f9-4ea7-b3aa-880025ff271e · outbound

This paper cites Constrained neural networks for approx- imate nonlinear model predictive control.

Optimal Navigation in Microfluidics via the Optimization of a Discrete Loss Constrained neural networks for approx- imate nonlinear model predictive control

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:37:12.729057Z

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Observation f8ceba83-3a36-4594-a097-a282e4efdbee · outbound

This paper cites Pontryagin differentiable programming: An end-to-end learning and control framework.Advances in Neural Information Processing Systems, 33:7979–7992, 2020.

Optimal Navigation in Microfluidics via the Optimization of a Discrete Loss Pontryagin differentiable programming: An end-to-end learning and control framework.Advances in Neural Information Processing Systems, 33:7979–7992, 2020

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:37:12.711646Z

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Observation ab482035-0516-4b95-ba68-ab5cba10853e · outbound

This paper cites Differentiable mpc for end-to-end planning and control.Advances in neural information processing systems, 31, 2018.

Optimal Navigation in Microfluidics via the Optimization of a Discrete Loss Differentiable mpc for end-to-end planning and control.Advances in neural information processing systems, 31, 2018

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:37:12.693827Z

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Observation 2586e619-22bb-4ff8-8296-c590f4800f3d · outbound

This paper cites Efficient representation and approximation of model predictive control laws via deep learning.IEEE Transactions on Cybernetics, 50(9):3866–3878, 2020.

Optimal Navigation in Microfluidics via the Optimization of a Discrete Loss Efficient representation and approximation of model predictive control laws via deep learning.IEEE Transactions on Cybernetics, 50(9):3866–3878, 2020

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:37:12.675175Z

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Observation 0765e7e7-3462-46f9-b7e5-373f60b77732 · outbound

This paper cites Automatic differentiation in machine learning: a survey.Journal of Marchine Learning Research, 18:1– 43, 2018.

Optimal Navigation in Microfluidics via the Optimization of a Discrete Loss Automatic differentiation in machine learning: a survey.Journal of Marchine Learning Research, 18:1– 43, 2018

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:37:12.656232Z

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Observation a52ff76b-3df2-4057-87b6-91cea523a531 · outbound

This paper cites A formulation of nonlinear model predictive control using automatic differentiation.Journal of Process Control, 15(8):851–858, 2005.

Optimal Navigation in Microfluidics via the Optimization of a Discrete Loss A formulation of nonlinear model predictive control using automatic differentiation.Journal of Process Control, 15(8):851–858, 2005

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:37:12.638484Z

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Observation 941ef2c4-9e32-4e13-8a23-380df32ff453 · outbound

This paper cites Neural ODEs as Feedback Policies for Nonlinear Optimal Control.

Optimal Navigation in Microfluidics via the Optimization of a Discrete Loss Neural ODEs as Feedback Policies for Nonlinear Optimal Control

Reference 21

Resolution: verified exact; local_arxiv, observed 2026-08-15T19:37:12.038758Z

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Observation ff6f18c6-493c-42cb-ab6f-72ae4426f485 · outbound

This paper cites Neural ordinary differ- ential equations.Advances in neural information processing systems, 31, 2018.

Optimal Navigation in Microfluidics via the Optimization of a Discrete Loss Neural ordinary differ- ential equations.Advances in neural information processing systems, 31, 2018

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:37:12.620826Z

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Observation a93d8db4-35e3-454d-af29-1a127f0f6731 · outbound

This paper cites Second-order neural ode optimizer.Ad- vances in Neural Information Processing Systems, 34:25267–25279, 2021.

Optimal Navigation in Microfluidics via the Optimization of a Discrete Loss Second-order neural ode optimizer.Ad- vances in Neural Information Processing Systems, 34:25267–25279, 2021

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:37:12.600887Z

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Observation ec10912e-de49-4fe6-bff8-88cfba26f1f2 · outbound

This paper cites Data-driven optimal prediction with control.

Optimal Navigation in Microfluidics via the Optimization of a Discrete Loss Data-driven optimal prediction with control

Reference 24

Resolution: verified exact; local_arxiv, observed 2026-08-15T19:37:12.013178Z

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Observation 512ba4da-cca8-4211-acf5-33e89208918e · outbound

This paper cites Recent advances in parameteridentification techniques for ode.

Optimal Navigation in Microfluidics via the Optimization of a Discrete Loss Recent advances in parameteridentification techniques for ode

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:37:12.583612Z

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Observation 2c5b5270-8fec-4387-8c55-a948e3c97643 · outbound

This paper cites Sampling-based algorithms for optimal motion planning.The international journal of robotics research, 30(7):846–894, 2011.

Optimal Navigation in Microfluidics via the Optimization of a Discrete Loss Sampling-based algorithms for optimal motion planning.The international journal of robotics research, 30(7):846–894, 2011

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:37:12.567282Z

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Observation cd83052b-bb8d-46b2-bba1-8cea91959d18 · outbound

This paper cites Sampling-based algorithms for optimal motion planning using closed-loop prediction.

Optimal Navigation in Microfluidics via the Optimization of a Discrete Loss Sampling-based algorithms for optimal motion planning using closed-loop prediction

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:37:12.550311Z

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Observation fda616de-70b2-4350-b009-3e297fe08f69 · outbound

This paper cites An adaptive sampling algorithm with dynamic iterative probability adjustment incorporating positional information.Entropy, 26(6):451, 2024.

Optimal Navigation in Microfluidics via the Optimization of a Discrete Loss An adaptive sampling algorithm with dynamic iterative probability adjustment incorporating positional information.Entropy, 26(6):451, 2024

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:37:12.530829Z

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Observation 76f9cfdd-ec70-41c5-9746-9e35cd647b99 · outbound

This paper cites MIT press, 2018.

Optimal Navigation in Microfluidics via the Optimization of a Discrete Loss MIT press, 2018

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-15T19:37:11.716846Z

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Observation 8ee0a41e-e9d0-4a5c-8240-ab631b96ddbd · outbound

This paper cites Deep reinforcement learning based mobile robot navigation: A review.Tsinghua Science and Technology, 26(5):674–691, 2021.

Optimal Navigation in Microfluidics via the Optimization of a Discrete Loss Deep reinforcement learning based mobile robot navigation: A review.Tsinghua Science and Technology, 26(5):674–691, 2021

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:37:12.502445Z

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Observation 79cbda8a-c116-4a04-95f0-f5ef522ee821 · outbound

This paper cites Glider soaring via reinforcement learning in the field.Nature, 562(7726):236–239, 2018.

Optimal Navigation in Microfluidics via the Optimization of a Discrete Loss Glider soaring via reinforcement learning in the field.Nature, 562(7726):236–239, 2018

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-15T19:37:11.726676Z

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Observation b7b742d7-f90a-4635-9265-a5cef23d23ef · outbound

This paper cites Efficient collective swimming by harness- ing vortices through deep reinforcement learning.Proceedings of the National Academy of Sciences, 115(23):5849–5854, 2018.

Optimal Navigation in Microfluidics via the Optimization of a Discrete Loss Efficient collective swimming by harness- ing vortices through deep reinforcement learning.Proceedings of the National Academy of Sciences, 115(23):5849–5854, 2018

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:37:12.474765Z

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Observation e3e68a85-d88d-46ac-9f77-59a4cb95e352 · outbound

This paper cites Learning efficient navigation in vortical flow fields.Nature communications, 12(1):7143, 2021.

Optimal Navigation in Microfluidics via the Optimization of a Discrete Loss Learning efficient navigation in vortical flow fields.Nature communications, 12(1):7143, 2021

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-15T19:37:11.736162Z

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Observation 99b3fbc0-a678-4ade-8c56-9729296d2394 · outbound

This paper cites Flow navigation by smart microswimmers via reinforcement learning.Physical review letters, 118(15):158004, 2017.

Optimal Navigation in Microfluidics via the Optimization of a Discrete Loss Flow navigation by smart microswimmers via reinforcement learning.Physical review letters, 118(15):158004, 2017

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:37:12.446550Z

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Observation 3cfa0f1f-f547-48df-ba12-dbfea4f8f7f7 · outbound

This paper cites an unresolved cited work.

Optimal Navigation in Microfluidics via the Optimization of a Discrete Loss Unresolved cited work

Reference 35

Resolution: unresolved; raw_fallback, observed 2026-08-15T19:37:12.429612Z

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Observation 2e73cff8-b5ad-4a8a-8f9f-ff3c0bcb0e16 · outbound

This paper cites Reinforcement learning of optimal active particle navigation.New Journal of Physics, 24(7):073042, 2022.

Optimal Navigation in Microfluidics via the Optimization of a Discrete Loss Reinforcement learning of optimal active particle navigation.New Journal of Physics, 24(7):073042, 2022

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:37:12.412026Z

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Observation 74a401b7-8520-4a87-845b-461539dbd27d · outbound

This paper cites Optimal navigation of magnetic artificial microswimmers in blood capillaries with deep reinforcement learning.

Optimal Navigation in Microfluidics via the Optimization of a Discrete Loss Optimal navigation of magnetic artificial microswimmers in blood capillaries with deep reinforcement learning

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-15T19:37:11.756624Z

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Observation 5e993e50-4578-44ac-ba24-51e8739f4dc1 · outbound

This paper cites Point-to-point navigation of a fish-like swimmer in a vortical flow with deep reinforcement learning.Frontiers in Physics, 10:870273, 2022.

Optimal Navigation in Microfluidics via the Optimization of a Discrete Loss Point-to-point navigation of a fish-like swimmer in a vortical flow with deep reinforcement learning.Frontiers in Physics, 10:870273, 2022

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:37:12.395101Z

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No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 9933e333-bef9-4926-80ec-c23289991ece · outbound

This paper cites Deep reinforcement learning-based automatic exploration for navigation in unknown environment.IEEE transactions on neural networks and learning systems, 31(6):2064–2076, 2019.

Optimal Navigation in Microfluidics via the Optimization of a Discrete Loss Deep reinforcement learning-based automatic exploration for navigation in unknown environment.IEEE transactions on neural networks and learning systems, 31(6):2064–2076, 2019

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:37:12.376874Z

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Observation b0d27d81-d5c1-450e-836f-0c9fba48b289 · outbound

This paper cites Playing Atari with Deep Reinforcement Learning.

Optimal Navigation in Microfluidics via the Optimization of a Discrete Loss Playing Atari with Deep Reinforcement Learning

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-15T19:37:11.772284Z

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Observation a19e2967-8b91-4338-96ae-3186563bad2b · outbound

This paper cites Remember and forget for experience replay.

Optimal Navigation in Microfluidics via the Optimization of a Discrete Loss Remember and forget for experience replay

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:37:12.359475Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 5b4a23ce-f8dd-45f0-a7e0-53957767cea3 · outbound

This paper cites Model-based rein- forcement learning for closed-loop dynamic control of soft robotic manipulators.IEEE Transactions on Robotics, 35(1):124–134, 2018.

Optimal Navigation in Microfluidics via the Optimization of a Discrete Loss Model-based rein- forcement learning for closed-loop dynamic control of soft robotic manipulators.IEEE Transactions on Robotics, 35(1):124–134, 2018

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:37:12.342126Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 5a4604c9-ba8b-4f60-8e8f-e40a9d44c912 · outbound

This paper cites Reinforcement learning for robust trajectory design of inter- planetary missions.Journal of Guidance, Control, and Dynamics, 44(8):1440–1453, 2021.

Optimal Navigation in Microfluidics via the Optimization of a Discrete Loss Reinforcement learning for robust trajectory design of inter- planetary missions.Journal of Guidance, Control, and Dynamics, 44(8):1440–1453, 2021

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:37:12.325242Z

Source-reported events for the cited work

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Observation c3194706-6343-4757-bef3-7c4353862964 · outbound

This paper cites The artificial intelligence clinician learns optimal treatment strategies for sepsis in intensive care.Nature medicine, 24(11):1716–1720, 2018.

Optimal Navigation in Microfluidics via the Optimization of a Discrete Loss The artificial intelligence clinician learns optimal treatment strategies for sepsis in intensive care.Nature medicine, 24(11):1716–1720, 2018

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:37:12.308051Z

Source-reported events for the cited work

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Observation b83062a4-51fd-48d1-bd64-4bc4de52a414 · outbound

This paper cites Learning to drive in a day.

Optimal Navigation in Microfluidics via the Optimization of a Discrete Loss Learning to drive in a day

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:37:12.290099Z

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Observation b605428e-5fe6-4e8e-95b6-7e5b0ead83b0 · outbound

This paper cites Adam: A Method for Stochastic Optimization.

Optimal Navigation in Microfluidics via the Optimization of a Discrete Loss Adam: A Method for Stochastic Optimization

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-15T19:37:11.801727Z

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Observation 780ebcc3-871c-4396-b852-17d5f6d4eb02 · outbound

This paper cites Flow reconstruction by multiresolution optimization of a discrete loss with automatic differentiation.The European Physical Journal E, 46(7):59, 2023.

Optimal Navigation in Microfluidics via the Optimization of a Discrete Loss Flow reconstruction by multiresolution optimization of a discrete loss with automatic differentiation.The European Physical Journal E, 46(7):59, 2023

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:37:12.272628Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 03b558d8-bc12-4fbb-af0b-647484b53ce4 · outbound

This paper cites Chemotaxis of an elastic flagellated microrobot.Physical Review E, 108(4):044408, 2023.

Optimal Navigation in Microfluidics via the Optimization of a Discrete Loss Chemotaxis of an elastic flagellated microrobot.Physical Review E, 108(4):044408, 2023

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:37:12.254895Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 2115a400-3b33-427b-a460-9bf02a5e8113 · outbound

This paper cites Challenges and attempts to make intelligent microswimmers.

Optimal Navigation in Microfluidics via the Optimization of a Discrete Loss Challenges and attempts to make intelligent microswimmers

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:37:12.236266Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation b007d461-dc5a-471e-ac36-0e9f078f5ad7 · outbound

This paper cites an unresolved cited work.

Optimal Navigation in Microfluidics via the Optimization of a Discrete Loss Unresolved cited work

Reference 50

Resolution: unresolved; raw_fallback, observed 2026-08-15T19:37:12.217151Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 34d0c244-5a9b-4877-8978-0940d67574e1 · outbound

This paper cites Compiling machine learning programs via high-level tracing.Systems for Machine Learning, 4(9), 2018.

Optimal Navigation in Microfluidics via the Optimization of a Discrete Loss Compiling machine learning programs via high-level tracing.Systems for Machine Learning, 4(9), 2018

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-15T19:37:11.826418Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 146e2069-f879-40ad-a1b1-e79f98359cf9 · outbound

This paper cites Elsevier, 2000.

Optimal Navigation in Microfluidics via the Optimization of a Discrete Loss Elsevier, 2000

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-15T19:37:11.831954Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cda10d6b-d343-4d5c-8a91-ecd7cd748117 · outbound

This paper cites an unresolved cited work.

Optimal Navigation in Microfluidics via the Optimization of a Discrete Loss Unresolved cited work

Reference 53

Resolution: unresolved; raw_fallback, observed 2026-08-15T19:37:12.176102Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 70f93b69-07a0-4bca-afc7-14079f33d7e2 · outbound

This paper cites Fast magnetic micropropellers with random shapes.Nano letters, 15(10):7064–7070, 2015.

Optimal Navigation in Microfluidics via the Optimization of a Discrete Loss Fast magnetic micropropellers with random shapes.Nano letters, 15(10):7064–7070, 2015

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:37:12.158029Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 71082b2e-cd09-4f79-994c-833f5de57605 · outbound

This paper cites Simple swimmer at low reynolds number: Three linked spheres.

Optimal Navigation in Microfluidics via the Optimization of a Discrete Loss Simple swimmer at low reynolds number: Three linked spheres

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:37:12.139022Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation bac63f55-5c79-4a20-8fe9-ef324defa9cf · outbound

This paper cites Microswimmers learning chemo- taxis with genetic algorithms.Proceedings of the National Academy of Sciences, 118(19):e2019683118, 2021.

Optimal Navigation in Microfluidics via the Optimization of a Discrete Loss Microswimmers learning chemo- taxis with genetic algorithms.Proceedings of the National Academy of Sciences, 118(19):e2019683118, 2021

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:37:12.122348Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 5b643245-7158-4eca-862f-89405ab58089 · outbound

This paper cites CEM-GD: Cross-Entropy Method with Gradient Descent Planner for Model-Based Reinforcement Learning.

Optimal Navigation in Microfluidics via the Optimization of a Discrete Loss CEM-GD: Cross-Entropy Method with Gradient Descent Planner for Model-Based Reinforcement Learning

Reference 57

Resolution: verified exact; local_arxiv, observed 2026-08-15T19:37:11.933701Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 1ff5fcf1-971a-410c-872e-ba5c68a767d2 · outbound

This paper cites Policy invariance under reward transformations: Theory and application to reward shaping.

Optimal Navigation in Microfluidics via the Optimization of a Discrete Loss Policy invariance under reward transformations: Theory and application to reward shaping

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-15T19:37:11.863355Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bd491520-6b3d-4412-8bc2-6017dbc0b107 · outbound

This paper cites Artificial bacterial flagella: Fabrication and magnetic control.Applied Physics Letters, 94(6):064107, 2009.

Optimal Navigation in Microfluidics via the Optimization of a Discrete Loss Artificial bacterial flagella: Fabrication and magnetic control.Applied Physics Letters, 94(6):064107, 2009

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:37:12.092945Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 39420458-a4de-45de-8af8-137132769b89 · outbound

This paper cites Chiral colloidal molecules and observation of the propeller effect.Journal of the American Chemical Society, 135(33):12353–12359, 2013.

Optimal Navigation in Microfluidics via the Optimization of a Discrete Loss Chiral colloidal molecules and observation of the propeller effect.Journal of the American Chemical Society, 135(33):12353–12359, 2013

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:37:12.075252Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation a83a8bbd-aa3d-4653-95f1-c449bb26e9e5 · outbound

This paper cites Selecting for function: solution synthesis of magnetic nanopropellers.Nano letters, 13(11):5373–5378, 2013.

Optimal Navigation in Microfluidics via the Optimization of a Discrete Loss Selecting for function: solution synthesis of magnetic nanopropellers.Nano letters, 13(11):5373–5378, 2013

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:37:12.056880Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers