Deep Koopman operator framework for causal discovery in nonlinear dynamical systems | Paper Citation Record

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Deep Koopman operator framework for causal discovery in nonlinear dynamical systems write newline

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This paper cites D., Carroll, T., Pecora, L., Sidorowich, J., and Tsimring, L.

Deep Koopman operator framework for causal discovery in nonlinear dynamical systems D., Carroll, T., Pecora, L., Sidorowich, J., and Tsimring, L

Reference 2

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This paper cites M., Santoso, A., McGregor, S., and England, M.

Deep Koopman operator framework for causal discovery in nonlinear dynamical systems M., Santoso, A., McGregor, S., and England, M

Reference 3

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Deep Koopman operator framework for causal discovery in nonlinear dynamical systems Unresolved cited work

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This paper cites Theory of reproducing kernels.

Deep Koopman operator framework for causal discovery in nonlinear dynamical systems Theory of reproducing kernels

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Deep Koopman operator framework for causal discovery in nonlinear dynamical systems B., Lin, V., and Mahoney, M

Reference 6

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This paper cites D., Ibeling, D., and Icard, T.

Deep Koopman operator framework for causal discovery in nonlinear dynamical systems D., Ibeling, D., and Icard, T

Reference 7

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Deep Koopman operator framework for causal discovery in nonlinear dynamical systems F., and Fiedler, S

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This paper cites Dynamic Structural Causal Models.

Deep Koopman operator framework for causal discovery in nonlinear dynamical systems Dynamic Structural Causal Models

Reference 9

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Deep Koopman operator framework for causal discovery in nonlinear dynamical systems L., Brunton, B

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Deep Koopman operator framework for causal discovery in nonlinear dynamical systems L., Budišić, M., Kaiser, E., and Kutz, J

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Deep Koopman operator framework for causal discovery in nonlinear dynamical systems L., Zolman, N., Kutz, J

Reference 12

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This paper cites Tangent space causal inference: Leveraging vector fields for causal discovery in dynamical systems.

Deep Koopman operator framework for causal discovery in nonlinear dynamical systems Tangent space causal inference: Leveraging vector fields for causal discovery in dynamical systems

Reference 13

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Deep Koopman operator framework for causal discovery in nonlinear dynamical systems Unresolved cited work

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This paper cites Discovering causal relations and equations from data.

Deep Koopman operator framework for causal discovery in nonlinear dynamical systems Discovering causal relations and equations from data

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Deep Koopman operator framework for causal discovery in nonlinear dynamical systems and De Vito, E

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This paper cites EcoPro-LSTMv0: A Memory-based Machine Learning Approach to Predicting Ecosystem Dynamics across Time Scales in Mediterranean Environments.

Deep Koopman operator framework for causal discovery in nonlinear dynamical systems EcoPro-LSTMv0: A Memory-based Machine Learning Approach to Predicting Ecosystem Dynamics across Time Scales in Mediterranean Environments

Reference 17

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Deep Koopman operator framework for causal discovery in nonlinear dynamical systems D., and Peters, J

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This paper cites R., Pike, M., and Slawinska, J.

Deep Koopman operator framework for causal discovery in nonlinear dynamical systems R., Pike, M., and Slawinska, J

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This paper cites Generative learning for nonlinear dynamics.

Deep Koopman operator framework for causal discovery in nonlinear dynamical systems Generative learning for nonlinear dynamics

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Deep Koopman operator framework for causal discovery in nonlinear dynamical systems Unresolved cited work

Reference 21

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This paper cites H., Katz, R., and Krentz, Maria and, N.

Deep Koopman operator framework for causal discovery in nonlinear dynamical systems H., Katz, R., and Krentz, Maria and, N

Reference 22

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Deep Koopman operator framework for causal discovery in nonlinear dynamical systems Unresolved cited work

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Deep Koopman operator framework for causal discovery in nonlinear dynamical systems and Fedorov, A

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Deep Koopman operator framework for causal discovery in nonlinear dynamical systems Unresolved cited work

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This paper cites Causally-informed deep learning to improve climate models and projections.

Deep Koopman operator framework for causal discovery in nonlinear dynamical systems Causally-informed deep learning to improve climate models and projections

Reference 26

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Deep Koopman operator framework for causal discovery in nonlinear dynamical systems G., Barnett, N., and Crutchfield, J

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Deep Koopman operator framework for causal discovery in nonlinear dynamical systems An Equatorial Ocean Recharge Paradigm for ENSO

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Deep Koopman operator framework for causal discovery in nonlinear dynamical systems Spatiotemporal upscaling of sparse air-sea pco2 data via physics-informed transfer learning

Reference 29

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Deep Koopman operator framework for causal discovery in nonlinear dynamical systems Unresolved cited work

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Deep Koopman operator framework for causal discovery in nonlinear dynamical systems and Mezić, I

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Deep Koopman operator framework for causal discovery in nonlinear dynamical systems Learning dynamical systems via koopman operator regression in reproducing kernel hilbert spaces

Reference 32

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Deep Koopman operator framework for causal discovery in nonlinear dynamical systems N., Proctor, J

Reference 33

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Deep Koopman operator framework for causal discovery in nonlinear dynamical systems D., van Lier-Walqui, M., Santos, S., and Morrison, H

Reference 34

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Deep Koopman operator framework for causal discovery in nonlinear dynamical systems Unresolved cited work

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Deep Koopman operator framework for causal discovery in nonlinear dynamical systems R., Giannakis, D., Pike, M., and Slawinska, J

Reference 36

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Deep Koopman operator framework for causal discovery in nonlinear dynamical systems Unresolved cited work

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Deep Koopman operator framework for causal discovery in nonlinear dynamical systems Unresolved cited work

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Deep Koopman operator framework for causal discovery in nonlinear dynamical systems N., and Brunton, S

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Deep Koopman operator framework for causal discovery in nonlinear dynamical systems Unresolved cited work

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Deep Koopman operator framework for causal discovery in nonlinear dynamical systems Unresolved cited work

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Deep Koopman operator framework for causal discovery in nonlinear dynamical systems Unresolved cited work

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Deep Koopman operator framework for causal discovery in nonlinear dynamical systems Analysis of Fluid Flows via Spectral Properties of the Koopman Operator

Reference 43

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Deep Koopman operator framework for causal discovery in nonlinear dynamical systems and Gentine, P

Reference 44

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Deep Koopman operator framework for causal discovery in nonlinear dynamical systems Metaflux: Meta-learning global carbon fluxes from sparse spatiotemporal observations

Reference 45

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This paper cites ChaosBench: A Multi-Channel, Physics-Based Benchmark for Subseasonal-to-Seasonal Climate Prediction.

Deep Koopman operator framework for causal discovery in nonlinear dynamical systems ChaosBench: A Multi-Channel, Physics-Based Benchmark for Subseasonal-to-Seasonal Climate Prediction

Reference 46

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Deep Koopman operator framework for causal discovery in nonlinear dynamical systems Causal models for dynamical systems

Reference 47

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Deep Koopman operator framework for causal discovery in nonlinear dynamical systems The theory of signal detectability

Reference 48

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Deep Koopman operator framework for causal discovery in nonlinear dynamical systems and Recht, B

Reference 49

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This paper cites Analyzing Koopman approaches to physics-informed machine learning for long-term sea-surface temperature forecasting.

Deep Koopman operator framework for causal discovery in nonlinear dynamical systems Analyzing Koopman approaches to physics-informed machine learning for long-term sea-surface temperature forecasting

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Deep Koopman operator framework for causal discovery in nonlinear dynamical systems M., Fons, E., Ballinger, A

Reference 51

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Deep Koopman operator framework for causal discovery in nonlinear dynamical systems From Deterministic ODEs to Dynamic Structural Causal Models

Reference 52

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Deep Koopman operator framework for causal discovery in nonlinear dynamical systems Discovering contemporaneous and lagged causal relations in autocorrelated nonlinear time series datasets

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This paper cites D., Mu \ n oz-Mar \' , J., et al.

Deep Koopman operator framework for causal discovery in nonlinear dynamical systems D., Mu \ n oz-Mar \' , J., et al

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Deep Koopman operator framework for causal discovery in nonlinear dynamical systems and Crutchfield, J

Reference 55

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This paper cites Causal Discovery in Nonlinear Dynamical Systems using Koopman Operators , October 2024.

Deep Koopman operator framework for causal discovery in nonlinear dynamical systems Causal Discovery in Nonlinear Dynamical Systems using Koopman Operators , October 2024

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Deep Koopman operator framework for causal discovery in nonlinear dynamical systems O., Hyv \"a rinen, A., Kerminen, A., and Jordan, M

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This paper cites Detecting Causality in Complex Ecosystems.

Deep Koopman operator framework for causal discovery in nonlinear dynamical systems Detecting Causality in Complex Ecosystems

Reference 58

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Deep Koopman operator framework for causal discovery in nonlinear dynamical systems Unresolved cited work

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Deep Koopman operator framework for causal discovery in nonlinear dynamical systems and Picaut, J

Reference 60

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Deep Koopman operator framework for causal discovery in nonlinear dynamical systems Koopman Neural Forecaster for Time Series with Temporal Distribution Shifts

Reference 61

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Deep Koopman operator framework for causal discovery in nonlinear dynamical systems T., Smallman, T

Reference 62

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Deep Koopman operator framework for causal discovery in nonlinear dynamical systems Unresolved cited work

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Deep Koopman operator framework for causal discovery in nonlinear dynamical systems A., Tietsche, S., Mogensen, K., and Mayer, M

Reference 64

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