Numerical solutions of fixed points in two-dimensional Kuramoto-Sivashinsky equation expedited by reinforcement learning | Paper Citation Record

Observation 5037b467-4373-4ca2-a7fd-48b00f838f5f · outbound

This paper cites 1986 The Kuramoto-Sivashinsky equation: a bridge between PDE’s and dynamical systems.

Numerical solutions of fixed points in two-dimensional Kuramoto-Sivashinsky equation expedited by reinforcement learning 1986 The Kuramoto-Sivashinsky equation: a bridge between PDE’s and dynamical systems

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:58:39.682485Z

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No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 6667aef1-c019-4d21-928a-ffb2cfc436cf · outbound

This paper cites 2010 On the state space geometry of the Kuramoto– Sivashinsky flow in a periodic domain.

Numerical solutions of fixed points in two-dimensional Kuramoto-Sivashinsky equation expedited by reinforcement learning 2010 On the state space geometry of the Kuramoto– Sivashinsky flow in a periodic domain

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:58:39.668100Z

Source-reported events for the cited work

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Observation 98e90e68-65e5-490b-9593-8259ce471839 · outbound

This paper cites 1975 On the formation of dissipative structures in reaction-diffusion systems: Reductive perturbation approach.

Numerical solutions of fixed points in two-dimensional Kuramoto-Sivashinsky equation expedited by reinforcement learning 1975 On the formation of dissipative structures in reaction-diffusion systems: Reductive perturbation approach

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:58:39.653356Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation d5da5973-bbd9-42fc-a3f8-0d990d2ce8b5 · outbound

This paper cites 1988 Nonlinear analysis of hydrodynamic instability in laminar flames—I.

Numerical solutions of fixed points in two-dimensional Kuramoto-Sivashinsky equation expedited by reinforcement learning 1988 Nonlinear analysis of hydrodynamic instability in laminar flames—I

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:58:39.638333Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 77b94c55-53a3-4e48-a5d6-594a002950cb · outbound

This paper cites 2014 Nonlinear dynamics and chaos: With applications to physics, biology, chemistry, and engineering.

Numerical solutions of fixed points in two-dimensional Kuramoto-Sivashinsky equation expedited by reinforcement learning 2014 Nonlinear dynamics and chaos: With applications to physics, biology, chemistry, and engineering

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:58:39.622521Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation cc6b1379-4a3a-4350-b759-a5a4b0aee73f · outbound

This paper cites 1982 The strange attractor theory of turbulence.Annual Review of Fluid Mechanics 14, 347–364.

Numerical solutions of fixed points in two-dimensional Kuramoto-Sivashinsky equation expedited by reinforcement learning 1982 The strange attractor theory of turbulence.Annual Review of Fluid Mechanics 14, 347–364

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:58:39.567464Z

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No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 8dca96d7-fba2-4ec6-b3a2-e4e08cbd0ccb · outbound

This paper cites 2005 Recent progress in understanding the transition to turbulence in a pipe.

Numerical solutions of fixed points in two-dimensional Kuramoto-Sivashinsky equation expedited by reinforcement learning 2005 Recent progress in understanding the transition to turbulence in a pipe

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:58:39.470771Z

Source-reported events for the cited work

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Observation 25ba015e-3305-4b76-8f24-38b1961cca6d · outbound

This paper cites 2011 The Significance of Simple Invariant Solutions in Turbulent Flows.

Numerical solutions of fixed points in two-dimensional Kuramoto-Sivashinsky equation expedited by reinforcement learning 2011 The Significance of Simple Invariant Solutions in Turbulent Flows

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:58:39.420276Z

Source-reported events for the cited work

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Observation 36dbd4b8-c78b-4c34-bdc9-8939d2290518 · outbound

This paper cites 2021 Exact Coherent States and the Nonlinear Dynamics of Wall- Bounded Turbulent Flows.

Numerical solutions of fixed points in two-dimensional Kuramoto-Sivashinsky equation expedited by reinforcement learning 2021 Exact Coherent States and the Nonlinear Dynamics of Wall- Bounded Turbulent Flows

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:58:39.405330Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation c473ad43-5693-44d7-960f-7fec5a0a5587 · outbound

This paper cites 1986 The well-posedness of the Kuramoto–Sivashinsky equation.

Numerical solutions of fixed points in two-dimensional Kuramoto-Sivashinsky equation expedited by reinforcement learning 1986 The well-posedness of the Kuramoto–Sivashinsky equation

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:58:39.390327Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 869b016f-c78f-4727-8dd8-3dc7b0dae516 · outbound

This paper cites 1991 Predicting chaos for infinite dimensional dynamical systems: the Kuramoto-Sivashinsky equation, a case study..Proceedings of the National Academy of Sciences 88, 11129–11132.

Numerical solutions of fixed points in two-dimensional Kuramoto-Sivashinsky equation expedited by reinforcement learning 1991 Predicting chaos for infinite dimensional dynamical systems: the Kuramoto-Sivashinsky equation, a case study..Proceedings of the National Academy of Sciences 88, 11129–11132

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:58:39.375274Z

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Observation 8d6f8300-728c-4a3a-acb5-267d64a58e7e · outbound

This paper cites 1990 Back in the saddle again: a computer assisted study of the Kuramoto–Sivashinsky equation.

Numerical solutions of fixed points in two-dimensional Kuramoto-Sivashinsky equation expedited by reinforcement learning 1990 Back in the saddle again: a computer assisted study of the Kuramoto–Sivashinsky equation

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:58:39.360592Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 5ae2ebe9-3a8e-426e-b199-5027ad652920 · outbound

This paper cites 2019 Linearly recurrent autoencoder networks for learning dynamics.

Numerical solutions of fixed points in two-dimensional Kuramoto-Sivashinsky equation expedited by reinforcement learning 2019 Linearly recurrent autoencoder networks for learning dynamics

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:58:39.344787Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 4f8bec65-fc2e-4bfe-b4f2-bb84999cdc6f · outbound

This paper cites 1988 The steady states of the Kuramoto-Sivashinsky equation.

Numerical solutions of fixed points in two-dimensional Kuramoto-Sivashinsky equation expedited by reinforcement learning 1988 The steady states of the Kuramoto-Sivashinsky equation

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:58:39.329567Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation c16c3979-8e34-4a6b-b008-03a36ce0550b · outbound

This paper cites 2008 Unstable recurrent patterns in Kuramoto-Sivashinsky dynamics.

Numerical solutions of fixed points in two-dimensional Kuramoto-Sivashinsky equation expedited by reinforcement learning 2008 Unstable recurrent patterns in Kuramoto-Sivashinsky dynamics

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:58:39.313839Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 3bb9a68e-7eef-4662-b284-eef5466e38ed · outbound

This paper cites 2004 Jacobian-free Newton–Krylov methods: a survey of approaches and applications.

Numerical solutions of fixed points in two-dimensional Kuramoto-Sivashinsky equation expedited by reinforcement learning 2004 Jacobian-free Newton–Krylov methods: a survey of approaches and applications

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:58:39.298502Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 8602ba92-d3f9-4573-912b-d345486a5b74 · outbound

This paper cites Asymmetric Actor Critic for Image-Based Robot Learning.

Numerical solutions of fixed points in two-dimensional Kuramoto-Sivashinsky equation expedited by reinforcement learning Asymmetric Actor Critic for Image-Based Robot Learning

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-10T23:58:38.113225Z

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Observation 5251836a-eb0e-41f6-b2a1-8473a3de6cd6 · outbound

This paper cites An Actor-Critic Algorithm for Sequence Prediction.

Numerical solutions of fixed points in two-dimensional Kuramoto-Sivashinsky equation expedited by reinforcement learning An Actor-Critic Algorithm for Sequence Prediction

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-10T23:58:38.118420Z

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Observation de3bdbfc-b7c3-4026-84fe-b2ffbf84b18c · outbound

This paper cites Playing Atari with Deep Reinforcement Learning.

Numerical solutions of fixed points in two-dimensional Kuramoto-Sivashinsky equation expedited by reinforcement learning Playing Atari with Deep Reinforcement Learning

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-10T23:58:38.124139Z

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Observation 9de5cab0-b3aa-4eaa-a7c4-6e27e1a217a9 · outbound

This paper cites 2017 Mastering the game of go without human knowledge.

Numerical solutions of fixed points in two-dimensional Kuramoto-Sivashinsky equation expedited by reinforcement learning 2017 Mastering the game of go without human knowledge

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:58:39.283330Z

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Observation fbb04a38-a2bc-4709-84e9-1b0c8d100220 · outbound

This paper cites 2019 Learning to drive in a day.

Numerical solutions of fixed points in two-dimensional Kuramoto-Sivashinsky equation expedited by reinforcement learning 2019 Learning to drive in a day

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:58:39.202198Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 582199cd-a91a-439f-9cb9-203adabd08a6 · outbound

This paper cites 2020 Machine Learning for Fluid Mechanics.Annual Review of Fluid Mechanics 52, 477–508.

Numerical solutions of fixed points in two-dimensional Kuramoto-Sivashinsky equation expedited by reinforcement learning 2020 Machine Learning for Fluid Mechanics.Annual Review of Fluid Mechanics 52, 477–508

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:58:39.165261Z

Source-reported events for the cited work

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Observation 9fce1897-2c5d-45d9-b8f9-e0cedc40653b · outbound

This paper cites 2020 Deep reinforcement learning in fluid mechanics: A promising method for both active flow control and shape optimization.

Numerical solutions of fixed points in two-dimensional Kuramoto-Sivashinsky equation expedited by reinforcement learning 2020 Deep reinforcement learning in fluid mechanics: A promising method for both active flow control and shape optimization

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:58:39.149850Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 141497d2-bd9c-47b2-b0aa-bd65c6d0d613 · outbound

This paper cites 2019 Artificial neural networks trained through deep reinforcement learning discover control strategies for active flow control.Journal of fluid mechanics 865, 281–302.

Numerical solutions of fixed points in two-dimensional Kuramoto-Sivashinsky equation expedited by reinforcement learning 2019 Artificial neural networks trained through deep reinforcement learning discover control strategies for active flow control.Journal of fluid mechanics 865, 281–302

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:58:39.134644Z

Source-reported events for the cited work

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Observation f45641e7-35d4-4b55-842b-ef35427857a1 · outbound

This paper cites 2021 Robust flow control and optimal sensor placement using deep reinforcement learning.

Numerical solutions of fixed points in two-dimensional Kuramoto-Sivashinsky equation expedited by reinforcement learning 2021 Robust flow control and optimal sensor placement using deep reinforcement learning

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:58:39.119306Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 63235c03-6aeb-4619-a040-941350a851b3 · outbound

This paper cites 2022 Reinforcement-learning-based control of confined cylinder wakes with stability analyses.

Numerical solutions of fixed points in two-dimensional Kuramoto-Sivashinsky equation expedited by reinforcement learning 2022 Reinforcement-learning-based control of confined cylinder wakes with stability analyses

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:58:39.103339Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 1b33bc67-93bf-4031-a50d-e44df463b9ed · outbound

This paper cites 2023 Reinforcement-learning-based control of convectively unstable flows.

Numerical solutions of fixed points in two-dimensional Kuramoto-Sivashinsky equation expedited by reinforcement learning 2023 Reinforcement-learning-based control of convectively unstable flows

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:58:39.088537Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 1aa90a85-2b03-4c71-b6a7-144afafc292e · outbound

This paper cites 2023 Reinforcement learning of control strategies for reducing skin friction drag in a fully developed turbulent channel flow.

Numerical solutions of fixed points in two-dimensional Kuramoto-Sivashinsky equation expedited by reinforcement learning 2023 Reinforcement learning of control strategies for reducing skin friction drag in a fully developed turbulent channel flow

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:58:39.072924Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 93e8d33a-91c8-4679-bc0c-f8c6900453be · outbound

This paper cites 2020 Controlling Rayleigh–Bénard convection via reinforcement learning.

Numerical solutions of fixed points in two-dimensional Kuramoto-Sivashinsky equation expedited by reinforcement learning 2020 Controlling Rayleigh–Bénard convection via reinforcement learning

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:58:39.057829Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 2012e563-1665-4d4d-a8a0-1a15715abfc8 · outbound

This paper cites 2018 On-line building energy optimization using deep reinforcement learning.IEEE transactions on smart grid 10, 3698–3708.

Numerical solutions of fixed points in two-dimensional Kuramoto-Sivashinsky equation expedited by reinforcement learning 2018 On-line building energy optimization using deep reinforcement learning.IEEE transactions on smart grid 10, 3698–3708

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:58:39.041844Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation a6256ef9-afca-479f-a974-6569e8a969a1 · outbound

This paper cites 2020 Reinforcement learning for bluff body active flow control in experiments and simulations.

Numerical solutions of fixed points in two-dimensional Kuramoto-Sivashinsky equation expedited by reinforcement learning 2020 Reinforcement learning for bluff body active flow control in experiments and simulations

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:58:39.026013Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 3c88f585-fc38-476e-bfd1-b8e728388019 · outbound

This paper cites 2021 Symmetry reduction for deep reinforcement learning active control of chaotic spatiotemporal dynamics.

Numerical solutions of fixed points in two-dimensional Kuramoto-Sivashinsky equation expedited by reinforcement learning 2021 Symmetry reduction for deep reinforcement learning active control of chaotic spatiotemporal dynamics

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:58:38.928989Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 95dba1d2-b0c2-44bc-b596-9767bc6b6932 · outbound

This paper cites 2019 Control of chaotic systems by deep reinforcement learning.

Numerical solutions of fixed points in two-dimensional Kuramoto-Sivashinsky equation expedited by reinforcement learning 2019 Control of chaotic systems by deep reinforcement learning

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:58:38.815580Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 83193531-cfa6-476f-84e3-de7dfa0e75bc · outbound

This paper cites 2005 Fourth-order time-stepping for stiff PDEs.

Numerical solutions of fixed points in two-dimensional Kuramoto-Sivashinsky equation expedited by reinforcement learning 2005 Fourth-order time-stepping for stiff PDEs

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:58:38.709501Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 94b5bc5b-3f3f-4ff2-a5fd-7c139e6b1f67 · outbound

This paper cites 2015 An in-depth numerical study of the two- dimensional Kuramoto–Sivashinsky equation.

Numerical solutions of fixed points in two-dimensional Kuramoto-Sivashinsky equation expedited by reinforcement learning 2015 An in-depth numerical study of the two- dimensional Kuramoto–Sivashinsky equation

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:58:38.686218Z

Source-reported events for the cited work

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Observation 77383de3-25c4-4314-b9b7-6440955a8e5a · outbound

This paper cites Equilibria, periodic orbits and computing them.

Numerical solutions of fixed points in two-dimensional Kuramoto-Sivashinsky equation expedited by reinforcement learning Equilibria, periodic orbits and computing them

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-10T23:58:38.203781Z

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Observation cc4be6c4-f115-4a07-8da8-77be2247e9bd · outbound

This paper cites 2014 Deterministic policy gradient algorithms.

Numerical solutions of fixed points in two-dimensional Kuramoto-Sivashinsky equation expedited by reinforcement learning 2014 Deterministic policy gradient algorithms

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:58:38.669984Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 5cfb9522-4f53-4b1a-bca6-4d89d32d8169 · outbound

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Numerical solutions of fixed points in two-dimensional Kuramoto-Sivashinsky equation expedited by reinforcement learning Continuous control with deep reinforcement learning

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-10T23:58:38.254815Z

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Observation 92966f9a-cef8-4413-a81c-7efae5b1f973 · outbound

This paper cites 2017 Surfing the edge: using feedback control to find nonlinear solutions.

Numerical solutions of fixed points in two-dimensional Kuramoto-Sivashinsky equation expedited by reinforcement learning 2017 Surfing the edge: using feedback control to find nonlinear solutions

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:58:38.653802Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 7033feea-75ab-4069-8216-ca4aa81f852a · outbound

This paper cites A Tutorial on Bayesian Optimization.

Numerical solutions of fixed points in two-dimensional Kuramoto-Sivashinsky equation expedited by reinforcement learning A Tutorial on Bayesian Optimization

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Resolution: unresolved; no resolver link, observed 2026-08-10T23:58:38.334812Z

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Observation 0dbc8150-bb96-4de1-8a75-4fa371b7daea · outbound

This paper cites 2012 Practical bayesian optimization of machine learning algorithms.

Numerical solutions of fixed points in two-dimensional Kuramoto-Sivashinsky equation expedited by reinforcement learning 2012 Practical bayesian optimization of machine learning algorithms

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:58:38.637940Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation dd4d9f8f-737f-493f-9f8b-fa6eff6d0322 · outbound

This paper cites 2015 Scalable bayesian optimization using deep neural networks.

Numerical solutions of fixed points in two-dimensional Kuramoto-Sivashinsky equation expedited by reinforcement learning 2015 Scalable bayesian optimization using deep neural networks

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T23:58:38.621328Z

Source-reported events for the cited work

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