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Duane S. Boning | Fan-Keng Sun | Jayant R. Kalagnanam | Kyongmin Yeo | Dylan E. C. Grullon | J. Kalagnanam | D. Boning | K. Yeo | Fan-Keng Sun | D. E. C. Grullon
[1] E. Lorenz. Deterministic nonperiodic flow , 1963 .
[2] Miss A.O. Penney. (b) , 1974, The New Yale Book of Quotations.
[3] L. Glass,et al. Oscillation and chaos in physiological control systems. , 1977, Science.
[4] J. D. Farmer,et al. Chaotic attractors of an infinite-dimensional dynamical system , 1982 .
[5] Jonathan D. Cryer,et al. Time Series Analysis , 1986 .
[6] Andrew Harvey,et al. Forecasting, Structural Time Series Models and the Kalman Filter , 1990 .
[7] Steven H. Strogatz,et al. Nonlinear Dynamics and Chaos , 2024 .
[8] Jürgen Schmidhuber,et al. Long Short-Term Memory , 1997, Neural Computation.
[9] Jürgen Schmidhuber,et al. Learning to Forget: Continual Prediction with LSTM , 2000, Neural Computation.
[10] J. Sprott. Chaos and time-series analysis , 2001 .
[11] Neil J. Gordon,et al. A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking , 2002, IEEE Trans. Signal Process..
[12] Dongbin Xiu,et al. The Wiener-Askey Polynomial Chaos for Stochastic Differential Equations , 2002, SIAM J. Sci. Comput..
[13] Geir Evensen,et al. The Ensemble Kalman Filter: theoretical formulation and practical implementation , 2003 .
[14] Celso Grebogi,et al. Chemical and biological activity in open flows: A dynamical system approach , 2005 .
[15] Silvina Ponce Dawson,et al. Simple data-driven models of intracellular calcium dynamics with predictive power. , 2006, Physical review. E, Statistical, nonlinear, and soft matter physics.
[16] Jiguo Cao,et al. Parameter estimation for differential equations: a generalized smoothing approach , 2007 .
[17] Nasser M. Nasrabadi,et al. Pattern Recognition and Machine Learning , 2006, Technometrics.
[18] Habib N. Najm,et al. Uncertainty Quantification and Polynomial Chaos Techniques in Computational Fluid Dynamics , 2009 .
[19] J. Sprott. Elegant Chaos: Algebraically Simple Chaotic Flows , 2010 .
[20] C. Furusawa,et al. A Dynamical-Systems View of Stem Cell Biology , 2012, Science.
[21] George Sugihara,et al. Detecting Causality in Complex Ecosystems , 2012, Science.
[22] David R. Cox,et al. Time Series Analysis , 2012 .
[23] Pascal Vincent,et al. Representation Learning: A Review and New Perspectives , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[24] Leonidas S. Rompolis,et al. Forecasting the mean and volatility of stock returns from option prices , 2006 .
[25] Yoshua Bengio,et al. Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation , 2014, EMNLP.
[26] Max Welling,et al. Auto-Encoding Variational Bayes , 2013, ICLR.
[27] Michael G Fehlings,et al. Introduction to focus issue. , 2014, Spine.
[28] Steven L. Brunton,et al. On dynamic mode decomposition: Theory and applications , 2013, 1312.0041.
[29] Yoshua Bengio,et al. A Recurrent Latent Variable Model for Sequential Data , 2015, NIPS.
[30] Tyrus Berry,et al. Predicting chaotic time series with a partial model. , 2015, Physical review. E, Statistical, nonlinear, and soft matter physics.
[31] Geoffrey E. Hinton,et al. Deep Learning , 2015, Nature.
[32] Jimmy Ba,et al. Adam: A Method for Stochastic Optimization , 2014, ICLR.
[33] S. Brunton,et al. Discovering governing equations from data by sparse identification of nonlinear dynamical systems , 2015, Proceedings of the National Academy of Sciences.
[34] David M. Blei,et al. Variational Inference: A Review for Statisticians , 2016, ArXiv.
[35] Tyrus Berry,et al. Ensemble Kalman Filtering without a Model , 2016 .
[36] Thomas Erneux,et al. Introduction to Focus Issue: Time-delay dynamics. , 2017, Chaos.
[37] Christopher Burgess,et al. beta-VAE: Learning Basic Visual Concepts with a Constrained Variational Framework , 2016, ICLR 2016.
[38] Frank Hutter,et al. SGDR: Stochastic Gradient Descent with Warm Restarts , 2016, ICLR.
[39] Yu Zhang,et al. Unsupervised Learning of Disentangled and Interpretable Representations from Sequential Data , 2017, NIPS.
[40] Steven L. Brunton,et al. Deep learning for universal linear embeddings of nonlinear dynamics , 2017, Nature Communications.
[41] Stephan Mandt,et al. Disentangled Sequential Autoencoder , 2018, ICML.
[42] Nicholas Zabaras,et al. Bayesian Deep Convolutional Encoder-Decoder Networks for Surrogate Modeling and Uncertainty Quantification , 2018, J. Comput. Phys..
[43] Aaas News,et al. Book Reviews , 1893, Buffalo Medical and Surgical Journal.
[44] Steven L. Brunton,et al. Deep learning of dynamics and signal-noise decomposition with time-stepping constraints , 2018, J. Comput. Phys..
[45] Igor Melnyk,et al. Deep learning algorithm for data-driven simulation of noisy dynamical system , 2018, J. Comput. Phys..
[46] Kyongmin Yeo,et al. Short note on the behavior of recurrent neural network for noisy dynamical system , 2019, ArXiv.
[47] Paris Perdikaris,et al. Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations , 2019, J. Comput. Phys..
[48] Paris Perdikaris,et al. Adversarial Uncertainty Quantification in Physics-Informed Neural Networks , 2018, J. Comput. Phys..
[49] Petros Koumoutsakos,et al. Machine Learning for Fluid Mechanics , 2019, Annual Review of Fluid Mechanics.
[50] Allen G Hart,et al. Embedding and approximation theorems for echo state networks , 2019, Neural Networks.
[51] Nicholas Geneva,et al. Modeling the Dynamics of PDE Systems with Physics-Constrained Deep Auto-Regressive Networks , 2019, J. Comput. Phys..
[52] George Em Karniadakis,et al. Hidden fluid mechanics: Learning velocity and pressure fields from flow visualizations , 2020, Science.
[53] Luning Sun,et al. Surrogate modeling for fluid flows based on physics-constrained deep learning without simulation data , 2019, Computer Methods in Applied Mechanics and Engineering.
[54] Chryssostomos Chryssostomidis,et al. Learning functionals via LSTM neural networks for predicting vessel dynamics in extreme sea states , 2019, Proceedings of the Royal Society A.
[55] P. Alam,et al. H , 1887, High Explosives, Propellants, Pyrotechnics.
[56] P. Alam. ‘A’ , 2021, Composites Engineering: An A–Z Guide.