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Enoch Yeung
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2020 – today
- 2024
- [j6]Harris Clark
, Aleczander Taylor, Enoch Yeung
Modeling Control of Supercoiling Dynamics and Transcription Using DNA-Binding Proteins. IEEE Control. Syst. Lett. 8: 2253-2258 (2024) - [i24]Brian Charles Brown, Michael King, Sean Warnick, Enoch Yeung, David Grimsman:
An SVD-like Decomposition of Bounded-Input Bounded-Output Functions. CoRR abs/2404.00112 (2024) - 2022
- [j5]Mohammed Eslami
Prediction of whole-cell transcriptional response with machine learning. Bioinform. 38(2): 404-409 (2022) - [j4]Sai Pushpak Nandanoori
Data-Driven Operator Theoretic Methods for Phase Space Learning and Analysis. J. Nonlinear Sci. 32(6): 95 (2022) - [i23]Charles A. Johnson, Shara Balakrishnan, Enoch Yeung:
Heterogeneous mixtures of dictionary functions to approximate subspace invariance in Koopman operators. CoRR abs/2206.13585 (2022) - [i22]Shara Balakrishnan, Aqib Hasnain, Robert G. Egbert, Enoch Yeung:
Data-Driven Observability Decomposition with Koopman Operators for Optimization of Output Functions of Nonlinear Systems. CoRR abs/2210.09343 (2022) - [i21]Charles A. Johnson, Shara Balakrishnan, Enoch Yeung:
Learning Invariant Subspaces of Koopman Operators-Part 1: A Methodology for Demonstrating a Dictionary's Approximate Subspace Invariance. CoRR abs/2212.07358 (2022) - [i20]Charles A. Johnson, Shara Balakrishnan, Enoch Yeung:
Learning Invariant Subspaces of Koopman Operators-Part 2: Heterogeneous Dictionary Mixing to Approximate Subspace Invariance. CoRR abs/2212.07365 (2022) - 2021
- [i19]Shara Balakrishnan, Aqib Hasnain, Robert G. Egbert, Enoch Yeung:
The Effect of Sensor Fusion on Data-Driven Learning of Koopman Operators. CoRR abs/2106.15091 (2021) - [i18]Sai Pushpak Nandanoori, Subhrajit Sinha, Enoch Yeung:
Data-Driven Operator Theoretic Methods for Phase Space Learning and Analysis. CoRR abs/2106.15678 (2021) - 2020
- [c26]Shara Balakrishnan, Aqib Hasnain, Nibodh Boddupalli, Dennis M. Joshy, Robert G. Egbert, Enoch Yeung:
Prediction of fitness in bacteria with causal jump dynamic mode decomposition. ACC 2020: 3749-3756 - [c25]Zhiyuan Liu, Guohui Ding, Lijun Chen
Towards Scalable Koopman Operator Learning: Convergence Rates and A Distributed Learning Algorithm. ACC 2020: 3983-3990 - [c24]Aqib Hasnain, Nibodh Boddupalli, Shara Balakrishnan, Enoch Yeung:
Steady state programming of controlled nonlinear systems via deep dynamic mode decomposition. ACC 2020: 4245-4251 - [c23]Sai Pushpak Nandanoori, Subhrajit Sinha, Enoch Yeung:
Data-Driven Operator Theoretic Methods for Global Phase Space Learning. ACC 2020: 4551-4557 - [c22]Subhrajit Sinha, Sai Pushpak Nandanoori, Enoch Yeung:
Computationally Efficient Learning of Large Scale Dynamical Systems: A Koopman Theoretic Approach. SmartGridComm 2020: 1-6 - [i17]Subhrajit Sinha, Sai Pushpak Nandanoori, Enoch Yeung:
Data Driven Online Learning of Power System Dynamics. CoRR abs/2003.05068 (2020) - [i16]Shara Balakrishnan, Aqib Hasnain, Nibodh Boddupalli, Dennis M. Joshy, Robert G. Egbert, Enoch Yeung:
Prediction of fitness in bacteria with causal jump dynamic mode decomposition. CoRR abs/2006.12726 (2020) - [i15]Subhrajit Sinha, Sai Pushpak Nandanoori, Enoch Yeung:
Computationally Efficient Learning of Large Scale Dynamical Systems: A Koopman Theoretic Approach. CoRR abs/2007.00835 (2020)
2010 – 2019
- 2019
- [j3]Panos Stinis, Tobias Hagge, Alexandre M. Tartakovsky, Enoch Yeung:
Enforcing constraints for interpolation and extrapolation in Generative Adversarial Networks. J. Comput. Phys. 397 (2019) - [c21]Enoch Yeung, Soumya Kundu, Nathan O. Hodas:
Learning Deep Neural Network Representations for Koopman Operators of Nonlinear Dynamical Systems. ACC 2019: 4832-4839 - [c20]Subhrajit Sinha, Umesh Vaidya
On Computation of Koopman Operator from Sparse Data. ACC 2019: 5519-5524 - [c19]Seda Aslan, Henry R. Halperin, Laura Olivieri, Narutoshi Hibino, Axel Krieger, Yue-Hin Loke, Paige Mass, Kevin Nelson, Enoch Yeung, Jed Johnson, Justin D. Opfermann, Hiroshi Matsushita, Takahiro Inoue:
Design and Simulation of Patient-Specific Tissue-Engineered Bifurcated Right Ventricle-Pulmonary Artery Grafts using Computational Fluid Dynamics. BIBE 2019: 1012-1018 - [c18]Aqib Hasnain, Diveena Becker, Atsede Siba, Narendra Maheshri, Ben Gordon, Chris Voigt, Enoch Yeung, Subhrajit Sinha, Yuval Dorfan, Amin Espah Borujeni, Yongjin Park, Paul Maschhoff
A data-driven method for quantifying the impact of a genetic circuit on its host. BioCAS 2019: 1-4 - [c17]Aqib Hasnain, Nibodh Boddupalli, Enoch Yeung:
Optimal reporter placement in sparsely measured genetic networks using the Koopman operator. CDC 2019: 19-24 - [c16]Nibodh Boddupalli, Aqib Hasnain, Sai Pushpak Nandanoori
Koopman Operators for Generalized Persistence of Excitation Conditions for Nonlinear Systems. CDC 2019: 8106-8111 - [i14]W. Brent Daniel, Enoch Yeung:
A Constructive Approach for One-Shot Training of Neural Networks Using Hypercube-Based Topological Coverings. CoRR abs/1901.02878 (2019) - [i13]Aqib Hasnain, Nibodh Boddupalli, Enoch Yeung:
Optimal reporter placement in sparsely measured genetic networks using the Koopman operator. CoRR abs/1906.00944 (2019) - [i12]Nibodh Boddupalli, Aqib Hasnain, Enoch Yeung:
Persistence of Excitation for Koopman Operator Represented Dynamical Systems. CoRR abs/1906.10274 (2019) - [i11]Subhrajit Sinha, Sai Pushpak Nandanoori, Enoch Yeung:
Online Learning of Dynamical Systems: An Operator Theoretic Approach. CoRR abs/1909.12520 (2019) - [i10]Subhrajit Sinha, Umesh Vaidya, Enoch Yeung:
Information Transfer in Dynamical Systems and Optimal Placement of Actuators and Sensors for Control of Non-equilibrium Dynamics. CoRR abs/1909.13369 (2019) - [i9]Zhiyuan Liu, Guohui Ding, Lijun Chen, Enoch Yeung:
Towards Scalable Koopman Operator Learning: Convergence Rates and A Distributed Learning Algorithm. CoRR abs/1909.13455 (2019) - [i8]Sai Pushpak Nandanoori, Subhrajit Sinha, Enoch Yeung:
Data-Driven Operator Theoretic Methods for Global Phase Space Learning. CoRR abs/1910.03011 (2019) - 2018
- [c15]Enoch Yeung, Zhiyuan Liu, Nathan O. Hodas:
A Koopman Operator Approach for Computing and Balancing Gramians for Discrete Time Nonlinear Systems. ACC 2018: 337-344 - [c14]Charles A. Johnson, Enoch Yeung:
A Class of Logistic Functions for Approximating State-Inclusive Koopman Operators. ACC 2018: 4803-4810 - [c13]Zhiyuan Liu, Soumya Kundu, Lijun Chen
Decomposition of Nonlinear Dynamical Systems Using Koopman Gramians. ACC 2018: 4811-4818 - [c12]Pengcheng You, John Z. F. Pang, Enoch Yeung:
Stabilization of Power Networks via Market Dynamics. e-Energy 2018: 139-145 - [c11]Abdullah Al Maruf, Soumya Kundu, Enoch Yeung, Marian Anghel:
Decomposition of Nonlinear Dynamical Networks Via Comparison Systems. ECC 2018: 190-196 - [i7]Panos Stinis, Tobias Hagge, Alexandre M. Tartakovsky, Enoch Yeung:
Enforcing constraints for interpolation and extrapolation in Generative Adversarial Networks. CoRR abs/1803.08182 (2018) - 2017
- [j2]Ye Yuan, Anurag Rai, Enoch Yeung, Guy-Bart Stan
A Minimal Realization Technique for the Dynamical Structure Function of a Class of LTI Systems. IEEE Trans. Control. Netw. Syst. 4(2): 301-311 (2017) - [c10]Enoch Yeung, David R. Judi, W. Brent Daniel:
Contingency analysis of water distribution networks using quadratic sensitivity functions. ACC 2017: 228-233 - [i6]Enoch Yeung, Soumya Kundu, Nathan O. Hodas:
Learning Deep Neural Network Representations for Koopman Operators of Nonlinear Dynamical Systems. CoRR abs/1708.06850 (2017) - [i5]Enoch Yeung, Zhiyuan Liu, Nathan O. Hodas:
A Koopman Operator Approach for Computing and Balancing Gramians for Discrete Time Nonlinear Systems. CoRR abs/1709.08712 (2017) - [i4]Zhiyuan Liu, Soumya Kundu, Lijun Chen, Enoch Yeung:
Decomposition of Nonlinear Dynamical Systems Using Koopman Gramians. CoRR abs/1710.01719 (2017) - [i3]Tobias Hagge, Panos Stinis, Enoch Yeung, Alexandre M. Tartakovsky:
Solving differential equations with unknown constitutive relations as recurrent neural networks. CoRR abs/1710.02242 (2017) - [i2]Charles A. Johnson, Enoch Yeung:
A Class of Logistic Functions for Approximating State-Inclusive Koopman Operators. CoRR abs/1712.03132 (2017) - 2015
- [c9]Enoch Yeung, Jongmin Kim
Global network identification from reconstructed dynamical structure subnetworks: Applications to biochemical reaction networks. CDC 2015: 881-888 - 2014
- [c8]Enoch Yeung, Andrew Ng, Jongmin Kim
Modeling the effects of compositional context on promoter activity in an E. coli extract based transcription-translation system. CDC 2014: 5405-5412 - 2013
- [j1]Mengran Xue, Enoch Yeung, Anurag Rai, Sandip Roy, Yan Wan
Initial-Condition Estimation in Network Synchronization Processes: Algebraic and Graphical Characterizations of the Estimator. Complex Syst. 21(4) (2013) - [c7]Enoch Yeung, James L. Beck, Richard M. Murray
Modeling environmental disturbances with the chemical master equation. CDC 2013: 1384-1391 - [c6]Enoch Yeung, Jongmin Kim
Resource competition as a source of non-minimum phase behavior in transcription-translation systems. CDC 2013: 4060-4067 - 2012
- [c5]J. Adebayo, T. Southwick, Vasu Chetty, Enoch Yeung, Ye Yuan, Jorge M. Gonçalves
Dynamical structure function identifiability conditions enabling signal structure reconstruction. CDC 2012: 4635-4641 - [c4]Enoch Yeung, Jongmin Kim
Quantifying crosstalk in biochemical systems. CDC 2012: 5528-5535 - 2011
- [c3]Enoch Yeung, Jorge M. Gonçalves
Mathematical relationships between representations of structure in linear interconnected dynamical systems. ACC 2011: 4348-4353 - [i1]Enoch Yeung, Jorge M. Gonçalves, Henrik Sandberg, Sean Warnick:
The Meaning of Structure in Interconnected Dynamic Systems. CoRR abs/1108.2755 (2011) - 2010
- [c2]Enoch Yeung, Jorge M. Gonçalves
Representing structure in linear interconnected dynamical systems. CDC 2010: 6010-6015
2000 – 2009
- 2009
- [c1]Enoch Yeung, Jorge M. Gonçalves
Network structure preserving model reduction with weak a priori structural information. CDC 2009: 3256-3263
Coauthor Index
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